{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/resize","entry":"resize","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":108,"n_papers_ran":39,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":86,"n_samples_ran":26,"n_samples_fingerprinted":5,"n_places":118,"n_places_pointer_only":28,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":6,"ran_fixture":8,"ran":12,"unverified":60},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.03948","paper":"/paper/arxiv-2606-03948","title":"A Pocket Offline Model for Simultaneous Speech Translation as CUNI Submission to IWSLT 2026","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"ufal/SimulStreaming","path":"simulstreaming/whisper/simul_whisper/eow_detection.py","file_url":"https://github.com/ufal/SimulStreaming/blob/HEAD/simulstreaming/whisper/simul_whisper/eow_detection.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10c33e780e28779f","mcp_get_code":{"code_sha256":"10c33e780e28779f"}},{"arxiv_id":"2605.27990","paper":"/paper/arxiv-2605-27990","title":"Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Seunghyeok0715/CLAMP","path":"misc.py","file_url":"https://github.com/Seunghyeok0715/CLAMP/blob/HEAD/misc.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a200f7f175d39914","mcp_get_code":{"code_sha256":"a200f7f175d39914"}},{"arxiv_id":"2605.21550","paper":"/paper/arxiv-2605-21550","title":"PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"erdogant/findpeaks","path":"findpeaks/stats.py","file_url":"https://github.com/erdogant/findpeaks/blob/HEAD/findpeaks/stats.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2d4dfe47b6dd14f1","mcp_get_code":{"code_sha256":"2d4dfe47b6dd14f1"}},{"arxiv_id":"2604.08005","paper":"/paper/arxiv-2604-08005","title":"Preference Redirection via Attention Concentration: An Attack on Computer Use Agents","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Dominik5431/PRAC","path":"prac/models/glm_differentiable_processor.py","file_url":"https://github.com/Dominik5431/PRAC/blob/HEAD/prac/models/glm_differentiable_processor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1c335eea3b5ed973","mcp_get_code":{"code_sha256":"1c335eea3b5ed973"}},{"arxiv_id":"2601.08797","paper":"/paper/arxiv-2601-08797","title":"Dentalx: Context-Aware Dental Disease Detection With Radiographs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"zhiqin1998/DentYOLOX","path":"yolox/models/yolo_head.py","file_url":"https://github.com/zhiqin1998/DentYOLOX/blob/HEAD/yolox/models/yolo_head.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"42104fe5313ef574","mcp_get_code":{"code_sha256":"42104fe5313ef574"}},{"arxiv_id":"2510.23190","paper":"/paper/arxiv-2510-23190","title":"Evaluation of Vision-LLMs in Surveillance Video","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"pascalbenschopTU/VLLM_AnomalyRecognition","path":"datasets_AR/deep_privacy2/anonymize.py","file_url":"https://github.com/pascalbenschopTU/VLLM_AnomalyRecognition/blob/HEAD/datasets_AR/deep_privacy2/anonymize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6a518104eaabbf34","mcp_get_code":{"code_sha256":"6a518104eaabbf34"}},{"arxiv_id":"2508.17817","paper":"/paper/arxiv-2508-17817","title":"TemCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"Meiqi-Gong/TemCoCo","path":"utils.py","file_url":"https://github.com/Meiqi-Gong/TemCoCo/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6a9d77459cab804","mcp_get_code":{"code_sha256":"f6a9d77459cab804"}},{"arxiv_id":"2507.02581","paper":null,"title":"arXiv:2507.02581","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"Ashespt/S2DC","path":"Downstream/CC-CCII/trainer.py","file_url":"https://github.com/Ashespt/S2DC/blob/HEAD/Downstream/CC-CCII/trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b78931b304a8a9cb","mcp_get_code":{"code_sha256":"b78931b304a8a9cb"}},{"arxiv_id":"2506.00433","paper":"/paper/latent-wavelet-diffusion-enabling-4k-image","title":"Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free","date":"2025-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LuigiSigillo/LatentWaveletDiffusion","path":"src/helpers/cache_latent_codes.py","file_url":"https://github.com/LuigiSigillo/LatentWaveletDiffusion/blob/HEAD/src/helpers/cache_latent_codes.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"35767eedf8ca0430","mcp_get_code":{"code_sha256":"35767eedf8ca0430"}},{"arxiv_id":"2504.13561","paper":"/paper/weathergen-a-unified-diverse-weather","title":"WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion","date":"2025-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wuyang98/weathergen","path":"evaluate.py","file_url":"https://github.com/wuyang98/weathergen/blob/HEAD/evaluate.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8907b6307e2f248b","mcp_get_code":{"code_sha256":"8907b6307e2f248b"}},{"arxiv_id":"2503.07259","paper":"/paper/comodo-cross-modal-video-to-imu-distillation","title":"COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition","date":"2025-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Breezelled/COMODO","path":"comodo/utils/model_util.py","file_url":"https://github.com/Breezelled/COMODO/blob/HEAD/comodo/utils/model_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77589f4d86131356","mcp_get_code":{"code_sha256":"77589f4d86131356"}},{"arxiv_id":"2502.13363","paper":"/paper/pretrained-image-text-models-are-secretly","title":"Pretrained Image-Text Models are Secretly Video Captioners","date":"2025-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chunhuizng/mllm-video-captioner","path":"lavis/processors/functional_video.py","file_url":"https://github.com/chunhuizng/mllm-video-captioner/blob/HEAD/lavis/processors/functional_video.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"code_sha256_prefix":"f1f9c7dfbe0b3105","mcp_get_code":{"code_sha256":"f1f9c7dfbe0b3105"}},{"arxiv_id":"2502.10235","paper":"/paper/adapts-adapting-univariate-foundation-models","title":"AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting","date":"2025-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abenechehab/adapts","path":"src/adapts/utils/data_readers.py","file_url":"https://github.com/abenechehab/adapts/blob/HEAD/src/adapts/utils/data_readers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a14a6294ee5b993","mcp_get_code":{"code_sha256":"3a14a6294ee5b993"}},{"arxiv_id":"2502.06379","paper":"/paper/solving-linear-gaussian-bayesian-inverse","title":"Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo","date":"2025-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangbingliang2019/daps","path":"posterior_sample.py","file_url":"https://github.com/zhangbingliang2019/daps/blob/HEAD/posterior_sample.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a200f7f175d39914","mcp_get_code":{"code_sha256":"a200f7f175d39914"}},{"arxiv_id":"2502.01189","paper":"/paper/compressed-image-generation-with-denoising","title":"Compressed Image Generation with Denoising Diffusion Codebook Models","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DDCM-2025/ddcm-compressed-image-generation","path":"compressed_blind_face_restoration.py","file_url":"https://github.com/DDCM-2025/ddcm-compressed-image-generation/blob/HEAD/compressed_blind_face_restoration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"051480f785c72147","mcp_get_code":{"code_sha256":"051480f785c72147"}},{"arxiv_id":"2412.07720","paper":"/paper/acdit-interpolating-autoregressive","title":"ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thunlp/acdit","path":"transform_utils.py","file_url":"https://github.com/thunlp/acdit/blob/HEAD/transform_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1f9c7dfbe0b3105","mcp_get_code":{"code_sha256":"f1f9c7dfbe0b3105"}},{"arxiv_id":"2412.02241","paper":"/paper/fast-lidar-data-generation-with-rectified","title":"Fast LiDAR Data Generation with Rectified Flows","date":"2024-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kazuto1011/r2flow","path":"evaluate.py","file_url":"https://github.com/kazuto1011/r2flow/blob/HEAD/evaluate.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8907b6307e2f248b","mcp_get_code":{"code_sha256":"8907b6307e2f248b"}},{"arxiv_id":"2411.12951","paper":"/paper/on-the-consistency-of-video-large-language","title":"On the Consistency of Video Large Language Models in Temporal Comprehension","date":"2024-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minjoong507/consistency-of-video-llm","path":"timechat/processors/functional_video.py","file_url":"https://github.com/minjoong507/consistency-of-video-llm/blob/HEAD/timechat/processors/functional_video.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f1f9c7dfbe0b3105","mcp_get_code":{"code_sha256":"f1f9c7dfbe0b3105"}},{"arxiv_id":"2410.14980","paper":"/paper/dcdepth-progressive-monocular-depth","title":"DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain","date":"2024-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"w2kun/DCDepth","path":"networks/newcrf_utils.py","file_url":"https://github.com/w2kun/DCDepth/blob/HEAD/networks/newcrf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2410.10815","paper":"/paper/depth-any-video-with-scalable-synthetic-data","title":"Depth Any Video with Scalable Synthetic Data","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nightmare-n/DepthAnyVideo","path":"dav/utils/img_utils.py","file_url":"https://github.com/Nightmare-n/DepthAnyVideo/blob/HEAD/dav/utils/img_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f62dbc206ca86a39","mcp_get_code":{"code_sha256":"f62dbc206ca86a39"}},{"arxiv_id":"2407.16308","paper":"/paper/safnet-selective-alignment-fusion-network-for","title":"SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltkong218/SAFNet","path":"models/SAFNet.py","file_url":"https://github.com/ltkong218/SAFNet/blob/HEAD/models/SAFNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b44a1e73d2f3547f","mcp_get_code":{"code_sha256":"b44a1e73d2f3547f"}},{"arxiv_id":"2407.14126","paper":"/paper/mono-vifi-a-unified-learning-framework-for","title":"Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujf1226/mono-vifi","path":"networks/IFRNet.py","file_url":"https://github.com/liujf1226/mono-vifi/blob/HEAD/networks/IFRNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3073f3b71dc41f8a","mcp_get_code":{"code_sha256":"3073f3b71dc41f8a"}},{"arxiv_id":"2407.02013","paper":"/paper/digraf-diffeomorphic-graph-adaptive","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ipsitmantri/DiTASK","path":"models/segformer.py","file_url":"https://github.com/ipsitmantri/DiTASK/blob/HEAD/models/segformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"368118c367337eb7","mcp_get_code":{"code_sha256":"368118c367337eb7"}},{"arxiv_id":"2407.02013","paper":"/paper/digraf-diffeomorphic-graph-adaptive","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ipsitmantri/DiTASK","path":"models/base_decode_head.py","file_url":"https://github.com/ipsitmantri/DiTASK/blob/HEAD/models/base_decode_head.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ebb5fe1837669a8","mcp_get_code":{"code_sha256":"5ebb5fe1837669a8"}},{"arxiv_id":"2407.01521","paper":"/paper/improving-diffusion-inverse-problem-solving","title":"Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangbingliang2019/DAPS","path":"posterior_sample.py","file_url":"https://github.com/zhangbingliang2019/DAPS/blob/HEAD/posterior_sample.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a200f7f175d39914","mcp_get_code":{"code_sha256":"a200f7f175d39914"}},{"arxiv_id":"2406.07471","paper":"/paper/ophnet-a-large-scale-video-benchmark-for","title":"OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minghu0830/ophnet-benchmark","path":"baselines/task2/backbone/videomaev2/extract_tad_feature.py","file_url":"https://github.com/minghu0830/ophnet-benchmark/blob/HEAD/baselines/task2/backbone/videomaev2/extract_tad_feature.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d1dc1aa52fe2112","mcp_get_code":{"code_sha256":"8d1dc1aa52fe2112"}},{"arxiv_id":"2405.18406","paper":"/paper/raccoon-remove-add-and-change-video-content","title":"RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaehong31/raccoon","path":"p2v/mgie_train.py","file_url":"https://github.com/jaehong31/raccoon/blob/HEAD/p2v/mgie_train.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7760f26e090b6580","mcp_get_code":{"code_sha256":"7760f26e090b6580"}},{"arxiv_id":"2404.09486","paper":"/paper/mmcode-evaluating-multi-modal-code-large","title":"MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"happylkx/mmcode","path":"models.py","file_url":"https://github.com/happylkx/mmcode/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a94e7342cb05fb9","mcp_get_code":{"code_sha256":"4a94e7342cb05fb9"}},{"arxiv_id":"2404.03635","paper":"/paper/wordepth-variational-language-prior-for","title":"WorDepth: Variational Language Prior for Monocular Depth Estimation","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adonis-galaxy/wordepth","path":"src/networks/utils.py","file_url":"https://github.com/adonis-galaxy/wordepth/blob/HEAD/src/networks/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2404.03392","paper":"/paper/two-tricks-to-improve-unsupervised","title":"Boosting Unsupervised Segmentation Learning","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alperensari/segmentation-tricks","path":"utils_metric.py","file_url":"https://github.com/alperensari/segmentation-tricks/blob/HEAD/utils_metric.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d86ef5508de1f079","mcp_get_code":{"code_sha256":"d86ef5508de1f079"}},{"arxiv_id":"2404.00380","paper":"/paper/dhr-dual-features-driven-hierarchical","title":"DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation","date":"2024-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shjo-april/DHR","path":"core/uss/utils.py","file_url":"https://github.com/shjo-april/DHR/blob/HEAD/core/uss/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"72c53856189c88e9","mcp_get_code":{"code_sha256":"72c53856189c88e9"}},{"arxiv_id":"2403.20320","paper":"/paper/mtlora-a-low-rank-adaptation-approach-for","title":"MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scale-lab/mtlora","path":"models/segformer.py","file_url":"https://github.com/scale-lab/mtlora/blob/HEAD/models/segformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"368118c367337eb7","mcp_get_code":{"code_sha256":"368118c367337eb7"}},{"arxiv_id":"2403.20320","paper":"/paper/mtlora-a-low-rank-adaptation-approach-for","title":"MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scale-lab/mtlora","path":"models/base_decode_head.py","file_url":"https://github.com/scale-lab/mtlora/blob/HEAD/models/base_decode_head.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ebb5fe1837669a8","mcp_get_code":{"code_sha256":"5ebb5fe1837669a8"}},{"arxiv_id":"2403.20254","paper":"/paper/benchmarking-the-robustness-of-temporal","title":"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alvin-Zeng/temporal-robustness-benchmark","path":"extract_corrupted_feature_code/videomae_v2/copy_extract_tad_feature.py","file_url":"https://github.com/Alvin-Zeng/temporal-robustness-benchmark/blob/HEAD/extract_corrupted_feature_code/videomae_v2/copy_extract_tad_feature.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d1dc1aa52fe2112","mcp_get_code":{"code_sha256":"8d1dc1aa52fe2112"}},{"arxiv_id":"2403.13802","paper":"/paper/zigma-zigzag-mamba-diffusion-model","title":"ZigMa: A DiT-style Zigzag Mamba Diffusion Model","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CompVis/zigma","path":"datasets/video_utils.py","file_url":"https://github.com/CompVis/zigma/blob/HEAD/datasets/video_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f1f9c7dfbe0b3105","mcp_get_code":{"code_sha256":"f1f9c7dfbe0b3105"}},{"arxiv_id":"2403.01482","paper":"/paper/eagle-eigen-aggregation-learning-for-object","title":"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MICV-yonsei/EAGLE","path":"src_EAGLE/utils.py","file_url":"https://github.com/MICV-yonsei/EAGLE/blob/HEAD/src_EAGLE/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d86ef5508de1f079","mcp_get_code":{"code_sha256":"d86ef5508de1f079"}},{"arxiv_id":"2402.16050","paper":"/paper/lstp-language-guided-spatial-temporal-prompt","title":"Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigai-nlco/VideoTGB","path":"src/gadgets/functional_video.py","file_url":"https://github.com/bigai-nlco/VideoTGB/blob/HEAD/src/gadgets/functional_video.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1f9c7dfbe0b3105","mcp_get_code":{"code_sha256":"f1f9c7dfbe0b3105"}},{"arxiv_id":"2401.06013","paper":"/paper/surgical-dino-adapter-learning-of-foundation","title":"Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beileicui/surgicaldino","path":"surgicaldino.py","file_url":"https://github.com/beileicui/surgicaldino/blob/HEAD/surgicaldino.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ae874901ffd9a9c","mcp_get_code":{"code_sha256":"5ae874901ffd9a9c"}},{"arxiv_id":"2312.14091","paper":"/paper/hd-painter-high-resolution-and-prompt","title":"HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"picsart-ai-research/hd-painter","path":"metrics/utils.py","file_url":"https://github.com/picsart-ai-research/hd-painter/blob/HEAD/metrics/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36ce6a13ea5e3ada","mcp_get_code":{"code_sha256":"36ce6a13ea5e3ada"}},{"arxiv_id":"2312.00752","paper":"/paper/mamba-linear-time-sequence-modeling-with","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thearkaprava/ms-temba","path":"vim/models_MSTemba.py","file_url":"https://github.com/thearkaprava/ms-temba/blob/HEAD/vim/models_MSTemba.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"55bc02c6846634c4","mcp_get_code":{"code_sha256":"55bc02c6846634c4"}},{"arxiv_id":"2311.07166","paper":"/paper/nddepth-normal-distance-assisted-monocular-1","title":"NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShuweiShao/NDDepth","path":"Estimation/nddepth/networks/newcrf_utils.py","file_url":"https://github.com/ShuweiShao/NDDepth/blob/HEAD/Estimation/nddepth/networks/newcrf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2310.09276","paper":"/paper/transformer-based-multimodal-change-detection","title":"Transformer-based Multimodal Change Detection with Multitask Consistency Constraints","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qaz670756/mmcd","path":"models/bfcdnet.py","file_url":"https://github.com/qaz670756/mmcd/blob/HEAD/models/bfcdnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"11acef810dc5071c","mcp_get_code":{"code_sha256":"11acef810dc5071c"}},{"arxiv_id":"2310.05397","paper":"/paper/find-your-optimal-assignments-on-the-fly-a","title":"Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LINs-lab/HCFL","path":"create_c/make_imagenet_c_inception.py","file_url":"https://github.com/LINs-lab/HCFL/blob/HEAD/create_c/make_imagenet_c_inception.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2533a1c44e58bc03","mcp_get_code":{"code_sha256":"2533a1c44e58bc03"}},{"arxiv_id":"2309.17102","paper":"/paper/guiding-instruction-based-image-editing-via","title":"Guiding Instruction-based Image Editing via Multimodal Large Language Models","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-mgie","path":"mgie_train.py","file_url":"https://github.com/apple/ml-mgie/blob/HEAD/mgie_train.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7760f26e090b6580","mcp_get_code":{"code_sha256":"7760f26e090b6580"}},{"arxiv_id":"2309.14137","paper":"/paper/iebins-iterative-elastic-bins-for-monocular-1","title":"IEBins: Iterative Elastic Bins for Monocular Depth Estimation","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShuweiShao/IEBins","path":"iebins/networks/resize.py","file_url":"https://github.com/ShuweiShao/IEBins/blob/HEAD/iebins/networks/resize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ae874901ffd9a9c","mcp_get_code":{"code_sha256":"5ae874901ffd9a9c"}},{"arxiv_id":"2309.14137","paper":"/paper/iebins-iterative-elastic-bins-for-monocular-1","title":"IEBins: Iterative Elastic Bins for Monocular Depth Estimation","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShuweiShao/IEBins","path":"iebins/networks/newcrf_utils.py","file_url":"https://github.com/ShuweiShao/IEBins/blob/HEAD/iebins/networks/newcrf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2309.09256","paper":"/paper/lidar-data-synthesis-with-denoising-diffusion","title":"LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models","date":"2023-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kazuto1011/r2dm","path":"evaluate.py","file_url":"https://github.com/kazuto1011/r2dm/blob/HEAD/evaluate.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8907b6307e2f248b","mcp_get_code":{"code_sha256":"8907b6307e2f248b"}},{"arxiv_id":"2308.06112","paper":"/paper/lip2vec-efficient-and-robust-visual-speech","title":"Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YasserdahouML/Lip2Vec","path":"datasets/functional_video.py","file_url":"https://github.com/YasserdahouML/Lip2Vec/blob/HEAD/datasets/functional_video.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a42f16eadfb7718f","mcp_get_code":{"code_sha256":"a42f16eadfb7718f"}},{"arxiv_id":"2308.04008","paper":"/paper/coarse-to-fine-learning-compact","title":"Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feymanpriv/DOLG-paddle","path":"infer.py","file_url":"https://github.com/feymanpriv/DOLG-paddle/blob/HEAD/infer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"192cb22df1633f0e","mcp_get_code":{"code_sha256":"192cb22df1633f0e"}},{"arxiv_id":"2307.12612","paper":"/paper/less-is-more-focus-attention-for-efficient","title":"Less is More: Focus Attention for Efficient DETR","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linxid/Focus-DETR","path":"models/focus_detr/transforms.py","file_url":"https://github.com/linxid/Focus-DETR/blob/HEAD/models/focus_detr/transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fbb85c3da476c722","mcp_get_code":{"code_sha256":"fbb85c3da476c722"}},{"arxiv_id":"2306.01667","paper":"/paper/towards-in-context-scene-understanding","title":"Towards In-context Scene Understanding","date":"2023-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vpariza/open-hummingbird-eval","path":"hbird/utils/image_transformations.py","file_url":"https://github.com/vpariza/open-hummingbird-eval/blob/HEAD/hbird/utils/image_transformations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f64df2cf48096a0c","mcp_get_code":{"code_sha256":"f64df2cf48096a0c"}},{"arxiv_id":"2305.20086","paper":"/paper/understanding-and-mitigating-copying-in-1","title":"Understanding and Mitigating Copying in Diffusion Models","date":"2023-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"somepago/dcr","path":"diff_inference.py","file_url":"https://github.com/somepago/dcr/blob/HEAD/diff_inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"81a30031f1ae390e","mcp_get_code":{"code_sha256":"81a30031f1ae390e"}},{"arxiv_id":"2305.15581","paper":"/paper/unsupervised-semantic-correspondence-using","title":"Unsupervised Semantic Correspondence Using Stable Diffusion","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ubc-vision/LDM_correspondences","path":"eval/dataset.py","file_url":"https://github.com/ubc-vision/LDM_correspondences/blob/HEAD/eval/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3c8be19016fde613","mcp_get_code":{"code_sha256":"3c8be19016fde613"}},{"arxiv_id":"2304.09790","paper":"/paper/amt-all-pairs-multi-field-transforms-for","title":"AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation","date":"2023-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MCG-NKU/AMT","path":"networks/AMT-S.py","file_url":"https://github.com/MCG-NKU/AMT/blob/HEAD/networks/AMT-S.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3073f3b71dc41f8a","mcp_get_code":{"code_sha256":"3073f3b71dc41f8a"}},{"arxiv_id":"2303.14969","paper":"/paper/universal-few-shot-learning-of-dense","title":"Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gitgyun/visual_token_matching","path":"dataset/resize_buildings.py","file_url":"https://github.com/gitgyun/visual_token_matching/blob/HEAD/dataset/resize_buildings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a5e2f2172266d78","mcp_get_code":{"code_sha256":"8a5e2f2172266d78"}},{"arxiv_id":"2302.08149","paper":"/paper/urcdc-depth-uncertainty-rectified-cross","title":"URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation","date":"2023-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuweishao/urcdc-depth","path":"urcdc/networks/newcrf_utils.py","file_url":"https://github.com/shuweishao/urcdc-depth/blob/HEAD/urcdc/networks/newcrf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2212.07834","paper":"/paper/unsupervised-object-localization-observing","title":"Unsupervised Object Localization: Observing the Background to Discover Objects","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/found","path":"datasets/geometric_transforms.py","file_url":"https://github.com/valeoai/found/blob/HEAD/datasets/geometric_transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"51da33e6dc962567","mcp_get_code":{"code_sha256":"51da33e6dc962567"}},{"arxiv_id":"2211.09454","paper":"/paper/deepprivacy2-towards-realistic-full-body","title":"DeepPrivacy2: Towards Realistic Full-Body Anonymization","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hukkelas/deep_privacy2","path":"anonymize.py","file_url":"https://github.com/hukkelas/deep_privacy2/blob/HEAD/anonymize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6a518104eaabbf34","mcp_get_code":{"code_sha256":"6a518104eaabbf34"}},{"arxiv_id":"2210.09071","paper":"/paper/attention-attention-everywhere-monocular","title":"Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashutosh1807/pixelformer","path":"pixelformer/networks/utils.py","file_url":"https://github.com/ashutosh1807/pixelformer/blob/HEAD/pixelformer/networks/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2208.03792","paper":"/paper/domain-randomization-enhanced-depth","title":"Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects","date":"2022-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PKU-EPIC/DREDS","path":"CatePoseEstimation/networks/SwinDRNet.py","file_url":"https://github.com/PKU-EPIC/DREDS/blob/HEAD/CatePoseEstimation/networks/SwinDRNet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5e6e6db8817387f8","mcp_get_code":{"code_sha256":"5e6e6db8817387f8"}},{"arxiv_id":"2205.14620","paper":"/paper/ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pilot7747/sldl","path":"sldl/video/ifrnet.py","file_url":"https://github.com/pilot7747/sldl/blob/HEAD/sldl/video/ifrnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5b17e544cf23ff18","mcp_get_code":{"code_sha256":"5b17e544cf23ff18"}},{"arxiv_id":"2205.14620","paper":"/paper/ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltkong218/IFRNet","path":"models/IFRNet.py","file_url":"https://github.com/ltkong218/IFRNet/blob/HEAD/models/IFRNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3073f3b71dc41f8a","mcp_get_code":{"code_sha256":"3073f3b71dc41f8a"}},{"arxiv_id":"2204.13022","paper":"/paper/binding-actions-to-objects-in-world-models","title":"Binding Actions to Objects in World Models","date":"2022-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TolgaOk/Differentiable-Hard-Attention-Module","path":"Mnist_attention.py","file_url":"https://github.com/TolgaOk/Differentiable-Hard-Attention-Module/blob/HEAD/Mnist_attention.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c505f17060f182b6","mcp_get_code":{"code_sha256":"c505f17060f182b6"}},{"arxiv_id":"2204.12511","paper":"/paper/polyloss-a-polynomial-expansion-perspective-1","title":"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jahongir7174/MaskRCNN","path":"utils/util.py","file_url":"https://github.com/jahongir7174/MaskRCNN/blob/HEAD/utils/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6a94a5bc4d9e8f4","mcp_get_code":{"code_sha256":"e6a94a5bc4d9e8f4"}},{"arxiv_id":"2204.12484","paper":"/paper/vitpose-simple-vision-transformer-baselines","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JunkyByte/easy_ViTPose","path":"easy_ViTPose/vit_models/model.py","file_url":"https://github.com/JunkyByte/easy_ViTPose/blob/HEAD/easy_ViTPose/vit_models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"471e5bd20e25b2a2","mcp_get_code":{"code_sha256":"471e5bd20e25b2a2"}},{"arxiv_id":"2204.12484","paper":"/paper/vitpose-simple-vision-transformer-baselines","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaehyunnn/ViTPose_pytorch","path":"models/model.py","file_url":"https://github.com/jaehyunnn/ViTPose_pytorch/blob/HEAD/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"126e704199014f2d","mcp_get_code":{"code_sha256":"126e704199014f2d"}},{"arxiv_id":"2204.02574","paper":"/paper/focalclick-towards-practical-interactive","title":"FocalClick: Towards Practical Interactive Image Segmentation","date":"2022-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XavierCHEN34/ClickSEG","path":"isegm/model/is_hrnet_model.py","file_url":"https://github.com/XavierCHEN34/ClickSEG/blob/HEAD/isegm/model/is_hrnet_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"55bc02c6846634c4","mcp_get_code":{"code_sha256":"55bc02c6846634c4"}},{"arxiv_id":"2203.08414","paper":"/paper/unsupervised-semantic-segmentation-by-2","title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mhamilton723/STEGO","path":"src/train_segmentation.py","file_url":"https://github.com/mhamilton723/STEGO/blob/HEAD/src/train_segmentation.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29fed21d5b05ba11","mcp_get_code":{"code_sha256":"29fed21d5b05ba11"}},{"arxiv_id":"2203.01502","paper":"/paper/new-crfs-neural-window-fully-connected-crfs-1","title":"NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliyun/NeWCRFs","path":"newcrfs/networks/newcrf_utils.py","file_url":"https://github.com/aliyun/NeWCRFs/blob/HEAD/newcrfs/networks/newcrf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"9012d3fdd4cbaea1","mcp_get_code":{"code_sha256":"9012d3fdd4cbaea1"}},{"arxiv_id":"2201.01293","paper":"/paper/a-transformer-based-siamese-network-for","title":"A Transformer-Based Siamese Network for Change Detection","date":"2022-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/changeformer","path":"models/ChangeFormer.py","file_url":"https://github.com/wgcban/changeformer/blob/HEAD/models/ChangeFormer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11acef810dc5071c","mcp_get_code":{"code_sha256":"11acef810dc5071c"}},{"arxiv_id":"2111.14813","paper":"/paper/transweather-transformer-based-restoration-of","title":"TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeya-maria-jose/TransWeather","path":"transweather_model.py","file_url":"https://github.com/jeya-maria-jose/TransWeather/blob/HEAD/transweather_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11acef810dc5071c","mcp_get_code":{"code_sha256":"11acef810dc5071c"}},{"arxiv_id":"2107.02612","paper":"/paper/combining-efficientnet-and-vision","title":"Combining EfficientNet and Vision Transformers for Video Deepfake Detection","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection","path":"cross-efficient-vit/utils.py","file_url":"https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/blob/HEAD/cross-efficient-vit/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"441055127d2e4fa1","mcp_get_code":{"code_sha256":"441055127d2e4fa1"}},{"arxiv_id":"2106.13358","paper":"/paper/scalable-perception-action-communication","title":"Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks","date":"2021-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/VGAI","path":"dataset.py","file_url":"https://github.com/VITA-Group/VGAI/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbb96c1d0826496d","mcp_get_code":{"code_sha256":"cbb96c1d0826496d"}},{"arxiv_id":"2106.06560","paper":"/paper/hr-nas-searching-efficient-high-resolution","title":"HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/HR-NAS","path":"models/transformer.py","file_url":"https://github.com/dingmyu/HR-NAS/blob/HEAD/models/transformer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8fbff5c3395ac23","mcp_get_code":{"code_sha256":"e8fbff5c3395ac23"}},{"arxiv_id":"2104.03133","paper":"/paper/image-composition-assessment-with-saliency","title":"Image Composition Assessment with Saliency-augmented Multi-pattern Pooling","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/Image-Composition-Assessment-Dataset-CADB","path":"SAMPNet/cadb_dataset.py","file_url":"https://github.com/bcmi/Image-Composition-Assessment-Dataset-CADB/blob/HEAD/SAMPNet/cadb_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbb96c1d0826496d","mcp_get_code":{"code_sha256":"cbb96c1d0826496d"}},{"arxiv_id":"2103.15087","paper":"/paper/learning-a-sketch-tensor-space-for-image","title":"Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes","date":"2021-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ewrfcas/MST_inpainting","path":"src/model_inference.py","file_url":"https://github.com/ewrfcas/MST_inpainting/blob/HEAD/src/model_inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"efc17d7d22e7bdcf","mcp_get_code":{"code_sha256":"efc17d7d22e7bdcf"}},{"arxiv_id":"2103.13253","paper":"/paper/learning-versatile-neural-architectures-by","title":"Learning Versatile Neural Architectures by Propagating Network Codes","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/NCP","path":"models/supernet.py","file_url":"https://github.com/dingmyu/NCP/blob/HEAD/models/supernet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"e8fbff5c3395ac23","mcp_get_code":{"code_sha256":"e8fbff5c3395ac23"}},{"arxiv_id":"2005.09704","paper":"/paper/contextual-residual-aggregation-for-ultra","title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","date":"2020-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"duxingren14/Hifill-tensorflow","path":"ops.py","file_url":"https://github.com/duxingren14/Hifill-tensorflow/blob/HEAD/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d014eb2c7ea0869f","mcp_get_code":{"code_sha256":"d014eb2c7ea0869f"}},{"arxiv_id":"2004.10955","paper":"/paper/joint-bilateral-learning-for-real-time","title":"Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mousecpn/Joint-Bilateral-Learning","path":"model.py","file_url":"https://github.com/mousecpn/Joint-Bilateral-Learning/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbb96c1d0826496d","mcp_get_code":{"code_sha256":"cbb96c1d0826496d"}},{"arxiv_id":"2003.08608","paper":"/paper/depth-potentiality-aware-gated-attention","title":"DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JosephChenHub/DPANet","path":"lib/transform.py","file_url":"https://github.com/JosephChenHub/DPANet/blob/HEAD/lib/transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600f77ad92f7a6fc","mcp_get_code":{"code_sha256":"600f77ad92f7a6fc"}},{"arxiv_id":"2003.06800","paper":"/paper/os2d-one-stage-one-shot-object-detection-by","title":"OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features","date":"2020-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aosokin/os2d","path":"os2d/structures/transforms.py","file_url":"https://github.com/aosokin/os2d/blob/HEAD/os2d/structures/transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb8bbafb3b69dae8","mcp_get_code":{"code_sha256":"cb8bbafb3b69dae8"}},{"arxiv_id":"1909.05316","paper":"/paper/what-makes-a-good-story-designing-composite","title":"What Makes A Good Story? Designing Composite Rewards for Visual Storytelling","date":"2019-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JunjieHu/ReCo-RL","path":"src/decoder.py","file_url":"https://github.com/JunjieHu/ReCo-RL/blob/HEAD/src/decoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fb9ed9e53e30879","mcp_get_code":{"code_sha256":"4fb9ed9e53e30879"}},{"arxiv_id":"1908.06537","paper":"/paper/hyperpixel-flow-semantic-correspondence-with","title":"Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features","date":"2019-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juhongm999/hpf","path":"model/util.py","file_url":"https://github.com/juhongm999/hpf/blob/HEAD/model/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b4c530e8c7515bba","mcp_get_code":{"code_sha256":"b4c530e8c7515bba"}},{"arxiv_id":"1907.08175","paper":"/paper/on-the-evaluation-of-conditional-gans","title":"On the Evaluation of Conditional GANs","date":"2019-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/fjd","path":"datasets/dSprite_textures/dsprite_utils.py","file_url":"https://github.com/facebookresearch/fjd/blob/HEAD/datasets/dSprite_textures/dsprite_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"4f6056b2ba5bb865","mcp_get_code":{"code_sha256":"4f6056b2ba5bb865"}},{"arxiv_id":"1906.12340","paper":"/paper/using-self-supervised-learning-can-improve","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","date":"2019-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hendrycks/ss-ood","path":"multiclass_ood/opencv_functional.py","file_url":"https://github.com/hendrycks/ss-ood/blob/HEAD/multiclass_ood/opencv_functional.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f725a3000220ae9","mcp_get_code":{"code_sha256":"6f725a3000220ae9"}},{"arxiv_id":"1905.06368","paper":"/paper/190506368","title":"Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwydj/ultra_high_resolution_segmentation","path":"helper.py","file_url":"https://github.com/chenwydj/ultra_high_resolution_segmentation/blob/HEAD/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31b90680f5041dcc","mcp_get_code":{"code_sha256":"31b90680f5041dcc"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brjathu/deepcaps","path":"ensemble.py","file_url":"https://github.com/brjathu/deepcaps/blob/HEAD/ensemble.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aff1bfca07e0988e","mcp_get_code":{"code_sha256":"aff1bfca07e0988e"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mfarhadi98/Use-Capsule-Networks-for-kddcup","path":"ensemble.py","file_url":"https://github.com/mfarhadi98/Use-Capsule-Networks-for-kddcup/blob/HEAD/ensemble.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4a0420adfe205cb","mcp_get_code":{"code_sha256":"a4a0420adfe205cb"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mfarhadi98/Use-Capsule-Networks-for-kddcup","path":"load_datasets.py","file_url":"https://github.com/mfarhadi98/Use-Capsule-Networks-for-kddcup/blob/HEAD/load_datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db68514a2630cd72","mcp_get_code":{"code_sha256":"db68514a2630cd72"}},{"arxiv_id":"1904.07482","paper":"/paper/object-oriented-dynamics-learning-through","title":"Object-Oriented Dynamics Learning through Multi-Level Abstraction","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mig-zh/OODP","path":"evaluator.py","file_url":"https://github.com/mig-zh/OODP/blob/HEAD/evaluator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24df6333c88950eb","mcp_get_code":{"code_sha256":"24df6333c88950eb"}},{"arxiv_id":"1904.01906","paper":"/paper/what-is-wrong-with-scene-text-recognition","title":"What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmitrijsk/attentionhtr","path":"datasets/process_imgur5k.py","file_url":"https://github.com/dmitrijsk/attentionhtr/blob/HEAD/datasets/process_imgur5k.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e7cc5ea8a6820dc3","mcp_get_code":{"code_sha256":"e7cc5ea8a6820dc3"}},{"arxiv_id":"1812.08008","paper":"/paper/openpose-realtime-multi-person-2d-pose","title":"OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields","date":"2018-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daniegr/EfficientPose","path":"utils/helpers.py","file_url":"https://github.com/daniegr/EfficientPose/blob/HEAD/utils/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3c960223e2a7d66f","mcp_get_code":{"code_sha256":"3c960223e2a7d66f"}},{"arxiv_id":"1811.03194","paper":"/paper/ad-versarial-perceptual-ad-blocking-meets","title":"AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning","date":"2018-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ftramer/ad-versarial","path":"element-frame-based/sift/model.py","file_url":"https://github.com/ftramer/ad-versarial/blob/HEAD/element-frame-based/sift/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ceb2d6022efa952","mcp_get_code":{"code_sha256":"4ceb2d6022efa952"}},{"arxiv_id":"1806.07421","paper":"/paper/rise-randomized-input-sampling-for","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","date":"2018-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openvinotoolkit/openvino_xai","path":"openvino_xai/explainer/visualizer.py","file_url":"https://github.com/openvinotoolkit/openvino_xai/blob/HEAD/openvino_xai/explainer/visualizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"eabd217e202d8b8b","mcp_get_code":{"code_sha256":"eabd217e202d8b8b"}},{"arxiv_id":"1805.00907","paper":"/paper/glow-graph-lowering-compiler-techniques-for","title":"Glow: Graph Lowering Compiler Techniques for Neural Networks","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"pytorch/glow","path":"torch_glow/utils/torchvision_fake/transforms.py","file_url":"https://github.com/pytorch/glow/blob/HEAD/torch_glow/utils/torchvision_fake/transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"59659f8793b5e227","mcp_get_code":{"code_sha256":"59659f8793b5e227"}},{"arxiv_id":"1804.07723","paper":"/paper/image-inpainting-for-irregular-holes-using","title":"Image Inpainting for Irregular Holes Using Partial Convolutions","date":"2018-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cameltr/transref","path":"models/TransRef.py","file_url":"https://github.com/cameltr/transref/blob/HEAD/models/TransRef.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5d7bf5f17f71b491","mcp_get_code":{"code_sha256":"5d7bf5f17f71b491"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nashory/pggan-pytorch","path":"utils.py","file_url":"https://github.com/nashory/pggan-pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01bad44f39bf9fca","mcp_get_code":{"code_sha256":"01bad44f39bf9fca"}},{"arxiv_id":"1704.03155","paper":"/paper/east-an-efficient-and-accurate-scene-text","title":"EAST: An Efficient and Accurate Scene Text Detector","date":"2017-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yakhyo/east-pytorch","path":"detect.py","file_url":"https://github.com/yakhyo/east-pytorch/blob/HEAD/detect.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e55b33eadf6b16e5","mcp_get_code":{"code_sha256":"e55b33eadf6b16e5"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abods/generative_painting","path":"face-landmark/run_webcam.py","file_url":"https://github.com/abods/generative_painting/blob/HEAD/face-landmark/run_webcam.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53526c00d614f93a","mcp_get_code":{"code_sha256":"53526c00d614f93a"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yigitgunduc/tensor-to-image","path":"src/evaluate.py","file_url":"https://github.com/yigitgunduc/tensor-to-image/blob/HEAD/src/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2cc2ffd4bb1b28b8","mcp_get_code":{"code_sha256":"2cc2ffd4bb1b28b8"}},{"arxiv_id":"1610.02357","paper":"/paper/xception-deep-learning-with-depthwise","title":"Xception: Deep Learning with Depthwise Separable Convolutions","date":"2016-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amogh7joshi/engagement-detection","path":"util/baseimgops.py","file_url":"https://github.com/amogh7joshi/engagement-detection/blob/HEAD/util/baseimgops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c127f627ffe74343","mcp_get_code":{"code_sha256":"c127f627ffe74343"}},{"arxiv_id":"1609.03605","paper":"/paper/detecting-text-in-natural-image-with","title":"Detecting Text in Natural Image with Connectionist Text Proposal Network","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"courao/ocr.pytorch","path":"detect/ctpn_utils.py","file_url":"https://github.com/courao/ocr.pytorch/blob/HEAD/detect/ctpn_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38401bcff05e1d84","mcp_get_code":{"code_sha256":"38401bcff05e1d84"}},{"arxiv_id":"1606.02147","paper":"/paper/enet-a-deep-neural-network-architecture-for","title":"ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation","date":"2016-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alililia/ascend_E-NET","path":"src/dataset.py","file_url":"https://github.com/alililia/ascend_E-NET/blob/HEAD/src/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5d56ce0fb9020da6","mcp_get_code":{"code_sha256":"5d56ce0fb9020da6"}},{"arxiv_id":"1604.07316","paper":"/paper/end-to-end-learning-for-self-driving-cars","title":"End to End Learning for Self-Driving Cars","date":"2016-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaganadhg/tf2x_eval","path":"src/preprocess.py","file_url":"https://github.com/jaganadhg/tf2x_eval/blob/HEAD/src/preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7d435baa52bf195d","mcp_get_code":{"code_sha256":"7d435baa52bf195d"}},{"arxiv_id":"1604.07316","paper":"/paper/end-to-end-learning-for-self-driving-cars","title":"End to End Learning for Self-Driving Cars","date":"2016-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jm12138/car-behavioral-cloning-paddle","path":"car/utils.py","file_url":"https://github.com/jm12138/car-behavioral-cloning-paddle/blob/HEAD/car/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"732028197e9c0404","mcp_get_code":{"code_sha256":"732028197e9c0404"}},{"arxiv_id":"1511.00561","paper":"/paper/segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","date":"2015-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ArkaJU/SegNet---Chromosome","path":"load_data.py","file_url":"https://github.com/ArkaJU/SegNet---Chromosome/blob/HEAD/load_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ca2f73eb2c5eeed4","mcp_get_code":{"code_sha256":"ca2f73eb2c5eeed4"}},{"arxiv_id":"1506.08909","paper":"/paper/the-ubuntu-dialogue-corpus-a-large-dataset-1","title":"The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems","date":"2015-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangliuy/HybridNCM","path":"generation/decoder.py","file_url":"https://github.com/yangliuy/HybridNCM/blob/HEAD/generation/decoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4fb9ed9e53e30879","mcp_get_code":{"code_sha256":"4fb9ed9e53e30879"}},{"arxiv_id":"1409.6070","paper":"/paper/spatially-sparse-convolutional-neural","title":"Spatially-sparse convolutional neural networks","date":"2014-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simpleintel/jupyter","path":"utility.py","file_url":"https://github.com/simpleintel/jupyter/blob/HEAD/utility.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"08f488c000c83d60","mcp_get_code":{"code_sha256":"08f488c000c83d60"}},{"arxiv_id":"aaai_29391","paper":null,"title":"arXiv:aaai_29391","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Jiahuiqu/LDS2AE","path":"augment.py","file_url":"https://github.com/Jiahuiqu/LDS2AE/blob/HEAD/augment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1cf981627c1ef504","mcp_get_code":{"code_sha256":"1cf981627c1ef504"}},{"arxiv_id":"Wilson_SAFE_Sensitivity-Aware_Features_for_Out-of-Distribution_Object_Detection_ICCV_2023_paper","paper":null,"title":"arXiv:Wilson_SAFE_Sensitivity-Aware_Features_for_Out-of-Distribution_Object_Detection_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SamWilso/SAFE_Official","path":"SAFE/transforms_detr.py","file_url":"https://github.com/SamWilso/SAFE_Official/blob/HEAD/SAFE/transforms_detr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ffd4848fba38460a","mcp_get_code":{"code_sha256":"ffd4848fba38460a"}},{"arxiv_id":"Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper","paper":null,"title":"arXiv:Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yeh-yu-hsuan/BiFuse","path":"Utils/Equirec2Cube/EquirecRotate.py","file_url":"https://github.com/Yeh-yu-hsuan/BiFuse/blob/HEAD/Utils/Equirec2Cube/EquirecRotate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"516dc10db0ae9119","mcp_get_code":{"code_sha256":"516dc10db0ae9119"}},{"arxiv_id":"Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper","paper":null,"title":"arXiv:Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PKU-YuanGroup/WF-VAE","path":"causalvideovae/dataset/transform.py","file_url":"https://github.com/PKU-YuanGroup/WF-VAE/blob/HEAD/causalvideovae/dataset/transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"953228fb804cfc4a","mcp_get_code":{"code_sha256":"953228fb804cfc4a"}},{"arxiv_id":"Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper","paper":null,"title":"arXiv:Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PKU-YuanGroup/WF-VAE","path":"causalvideovae/dataset/video_dataset.py","file_url":"https://github.com/PKU-YuanGroup/WF-VAE/blob/HEAD/causalvideovae/dataset/video_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2c61e720647c372c","mcp_get_code":{"code_sha256":"2c61e720647c372c"}},{"arxiv_id":"Li_From_Contexts_to_Locality_Ultra-High_Resolution_Image_Segmentation_via_Locality-Aware_ICCV_2021_paper","paper":null,"title":"arXiv:Li_From_Contexts_to_Locality_Ultra-High_Resolution_Image_Segmentation_via_Locality-Aware_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"liqiokkk/FCtL","path":"helper.py","file_url":"https://github.com/liqiokkk/FCtL/blob/HEAD/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f690fd415e643231","mcp_get_code":{"code_sha256":"f690fd415e643231"}},{"arxiv_id":"Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"feiaxyt/EMC-Click","path":"isegm/model/is_hrnet_model.py","file_url":"https://github.com/feiaxyt/EMC-Click/blob/HEAD/isegm/model/is_hrnet_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"55bc02c6846634c4","mcp_get_code":{"code_sha256":"55bc02c6846634c4"}},{"arxiv_id":"Bergner_Token_Cropr_Faster_ViTs_for_Quite_a_Few_Tasks_CVPR_2025_paper","paper":null,"title":"arXiv:Bergner_Token_Cropr_Faster_ViTs_for_Quite_a_Few_Tasks_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"benbergner/cropr","path":"segm/utils/model_utils.py","file_url":"https://github.com/benbergner/cropr/blob/HEAD/segm/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bfb12c4e28f0d73b","mcp_get_code":{"code_sha256":"bfb12c4e28f0d73b"}},{"arxiv_id":"136760331","paper":null,"title":"arXiv:136760331","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chang9711/BIPS","path":"ECCV2022/utils.py","file_url":"https://github.com/chang9711/BIPS/blob/HEAD/ECCV2022/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01bad44f39bf9fca","mcp_get_code":{"code_sha256":"01bad44f39bf9fca"}},{"arxiv_id":"136670659","paper":null,"title":"arXiv:136670659","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PardoAlejo/MovieCuts","path":"src/transforms.py","file_url":"https://github.com/PardoAlejo/MovieCuts/blob/HEAD/src/transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c902ca295941540","mcp_get_code":{"code_sha256":"0c902ca295941540"}}]}