{"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/normalize-image","entry":"normalize_image","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":28,"n_papers_ran":10,"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":22,"n_samples_ran":7,"n_samples_fingerprinted":4,"n_places":28,"n_places_pointer_only":10,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":5,"unverified":15},"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":"2609.00666","paper":"/paper/arxiv-2609-00666","title":"DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"iLearn-Lab/MM26-DGNet","path":"utils.py","file_url":"https://github.com/iLearn-Lab/MM26-DGNet/blob/HEAD/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":"95b3a763a93f5c20","mcp_get_code":{"code_sha256":"95b3a763a93f5c20"}},{"arxiv_id":"2601.17586","paper":"/paper/arxiv-2601-17586","title":"Stylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"sdoerrich97/stylizing-vit","path":"stylizing_vit/util.py","file_url":"https://github.com/sdoerrich97/stylizing-vit/blob/HEAD/stylizing_vit/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":"bb34211615c52585","mcp_get_code":{"code_sha256":"bb34211615c52585"}},{"arxiv_id":"2510.20994","paper":"/paper/arxiv-2510-20994","title":"VESSA: Video-based objEct-centric Self-Supervised Adaptation for Visual Foundation Models","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"jesimonbarreto/VESSA","path":"src/CO3D_dataset.py","file_url":"https://github.com/jesimonbarreto/VESSA/blob/HEAD/src/CO3D_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e3e4cfaccce750d","mcp_get_code":{"code_sha256":"9e3e4cfaccce750d"}},{"arxiv_id":"2504.12186","paper":"/paper/comotion-concurrent-multi-person-3d-motion","title":"CoMotion: Concurrent Multi-person 3D Motion","date":"2025-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-comotion","path":"src/comotion_demo/utils/dataloading.py","file_url":"https://github.com/apple/ml-comotion/blob/HEAD/src/comotion_demo/utils/dataloading.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"29ef9539a2d223c9","mcp_get_code":{"code_sha256":"29ef9539a2d223c9"}},{"arxiv_id":"2503.10809","paper":"/paper/attacking-multimodal-os-agents-with-malicious","title":"Attacking Multimodal OS Agents with Malicious Image Patches","date":"2025-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIchberger/mip-against-agent","path":"src/mip_attack/model.py","file_url":"https://github.com/AIchberger/mip-against-agent/blob/HEAD/src/mip_attack/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":"0d40f28ae88b0be6","mcp_get_code":{"code_sha256":"0d40f28ae88b0be6"}},{"arxiv_id":"2501.09898","paper":"/paper/foundationstereo-zero-shot-stereo-matching","title":"FoundationStereo: Zero-Shot Stereo Matching","date":"2025-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/FoundationStereo","path":"core/foundation_stereo.py","file_url":"https://github.com/NVlabs/FoundationStereo/blob/HEAD/core/foundation_stereo.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1c5a401752019e06","mcp_get_code":{"code_sha256":"1c5a401752019e06"}},{"arxiv_id":"2406.14794","paper":"/paper/imageflownet-forecasting-multiscale","title":"ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenLiu-1996/ImageFlowNet","path":"src/datasets/brain_gbm.py","file_url":"https://github.com/ChenLiu-1996/ImageFlowNet/blob/HEAD/src/datasets/brain_gbm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f27d2f369a0fc269","mcp_get_code":{"code_sha256":"f27d2f369a0fc269"}},{"arxiv_id":"2403.17301","paper":"/paper/physical-3d-adversarial-attacks-against","title":"Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gandolfczjh/3D2Fool","path":"utils.py","file_url":"https://github.com/Gandolfczjh/3D2Fool/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"332a1ab65ab9e5a1","mcp_get_code":{"code_sha256":"332a1ab65ab9e5a1"}},{"arxiv_id":"2403.03463","paper":"/paper/flame-diffuser-grounded-wildfire-image","title":"FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIS-Clemson/FLAME_SD","path":"Flame_diffuser_binary_mask.py","file_url":"https://github.com/AIS-Clemson/FLAME_SD/blob/HEAD/Flame_diffuser_binary_mask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47aeec286315d0f5","mcp_get_code":{"code_sha256":"47aeec286315d0f5"}},{"arxiv_id":"2311.17539","paper":"/paper/the-effects-of-overparameterization-on","title":"Critical Influence of Overparameterization on Sharpness-aware Minimization","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"log-postech/sam-overparam","path":"input_pipeline.py","file_url":"https://github.com/log-postech/sam-overparam/blob/HEAD/input_pipeline.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70045adf02f53c3d","mcp_get_code":{"code_sha256":"70045adf02f53c3d"}},{"arxiv_id":"2310.15171","paper":"/paper/robodepth-robust-out-of-distribution-depth-1","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TJ-IPLab/DNet","path":"utils.py","file_url":"https://github.com/TJ-IPLab/DNet/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"332a1ab65ab9e5a1","mcp_get_code":{"code_sha256":"332a1ab65ab9e5a1"}},{"arxiv_id":"2309.01429","paper":"/paper/adapting-segment-anything-model-for-change","title":"Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggsding/sam-cd","path":"datasets/Levir_CD.py","file_url":"https://github.com/ggsding/sam-cd/blob/HEAD/datasets/Levir_CD.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac96c487a84274f0","mcp_get_code":{"code_sha256":"ac96c487a84274f0"}},{"arxiv_id":"2307.05541","paper":"/paper/high-fidelity-3d-hand-shape-reconstruction-1","title":"High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition","date":"2023-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tyluann/FreqHand","path":"main/model.py","file_url":"https://github.com/tyluann/FreqHand/blob/HEAD/main/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7366678241e8e560","mcp_get_code":{"code_sha256":"7366678241e8e560"}},{"arxiv_id":"2209.15256","paper":"/paper/s2p-state-conditioned-image-synthesis-for","title":"S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dsshim0125/s2p","path":"multiworld_custom/core/image_env.py","file_url":"https://github.com/dsshim0125/s2p/blob/HEAD/multiworld_custom/core/image_env.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f725932e0f8878f4","mcp_get_code":{"code_sha256":"f725932e0f8878f4"}},{"arxiv_id":"2206.07387","paper":"/paper/the-manifold-hypothesis-for-gradient-based-1","title":"The Manifold Hypothesis for Gradient-Based Explanations","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tml-tuebingen/explanations-manifold","path":"replicate-paper/other_datasets/util.py","file_url":"https://github.com/tml-tuebingen/explanations-manifold/blob/HEAD/replicate-paper/other_datasets/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1cf41bab864699c","mcp_get_code":{"code_sha256":"c1cf41bab864699c"}},{"arxiv_id":"2206.00272","paper":"/paper/vision-gnn-an-image-is-worth-graph-of-nodes","title":"Vision GNN: An Image is Worth Graph of Nodes","date":"2022-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lccol/vig-eo","path":"run_training.py","file_url":"https://github.com/lccol/vig-eo/blob/HEAD/run_training.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":"6f6471b9561767b8","mcp_get_code":{"code_sha256":"6f6471b9561767b8"}},{"arxiv_id":"2010.01412","paper":"/paper/sharpness-aware-minimization-for-efficiently-1","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","date":"2020-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/sam","path":"sam_jax/datasets/dataset_source_imagenet.py","file_url":"https://github.com/google-research/sam/blob/HEAD/sam_jax/datasets/dataset_source_imagenet.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":"245dfb545218b755","mcp_get_code":{"code_sha256":"245dfb545218b755"}},{"arxiv_id":"2004.10566","paper":"/paper/efficient-neighbourhood-consensus-networks","title":"Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions","date":"2020-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ignacio-rocco/sparse-ncnet","path":"lib/normalization.py","file_url":"https://github.com/ignacio-rocco/sparse-ncnet/blob/HEAD/lib/normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68162679b97bdf55","mcp_get_code":{"code_sha256":"68162679b97bdf55"}},{"arxiv_id":"2003.12059","paper":"/paper/correspondence-networks-with-adaptive","title":"Correspondence Networks with Adaptive Neighbourhood Consensus","date":"2020-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ActiveVisionLab/ANCNet","path":"lib/normalization.py","file_url":"https://github.com/ActiveVisionLab/ANCNet/blob/HEAD/lib/normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"68162679b97bdf55","mcp_get_code":{"code_sha256":"68162679b97bdf55"}},{"arxiv_id":"1910.10897","paper":"/paper/meta-world-a-benchmark-and-evaluation-for","title":"Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning","date":"2019-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CAVED123/METAWORLD","path":"metaworld/core/image_env.py","file_url":"https://github.com/CAVED123/METAWORLD/blob/HEAD/metaworld/core/image_env.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f725932e0f8878f4","mcp_get_code":{"code_sha256":"f725932e0f8878f4"}},{"arxiv_id":"1910.10672","paper":"/paper/gradslam-dense-slam-meets-automatic","title":"gradSLAM: Automagically differentiable SLAM","date":"2019-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gradslam/gradslam","path":"gradslam/datasets/datautils.py","file_url":"https://github.com/gradslam/gradslam/blob/HEAD/gradslam/datasets/datautils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ef8cf89974066d5","mcp_get_code":{"code_sha256":"8ef8cf89974066d5"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"see--/keras-centernet","path":"keras_centernet/models/networks/hourglass.py","file_url":"https://github.com/see--/keras-centernet/blob/HEAD/keras_centernet/models/networks/hourglass.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c6adb62e5178684","mcp_get_code":{"code_sha256":"9c6adb62e5178684"}},{"arxiv_id":"1904.04998","paper":"/paper/depth-from-videos-in-the-wild-unsupervised","title":"Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras","date":"2019-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bolianchen/pytorch_depth_from_videos_in_the_wild","path":"lib/img_processing.py","file_url":"https://github.com/bolianchen/pytorch_depth_from_videos_in_the_wild/blob/HEAD/lib/img_processing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"332a1ab65ab9e5a1","mcp_get_code":{"code_sha256":"332a1ab65ab9e5a1"}},{"arxiv_id":"1810.10510","paper":"/paper/neighbourhood-consensus-networks","title":"Neighbourhood Consensus Networks","date":"2018-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiwonCocoder/matching1","path":"lib/normalization.py","file_url":"https://github.com/JiwonCocoder/matching1/blob/HEAD/lib/normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68162679b97bdf55","mcp_get_code":{"code_sha256":"68162679b97bdf55"}},{"arxiv_id":"1806.01260","paper":"/paper/digging-into-self-supervised-monocular-depth","title":"Digging Into Self-Supervised Monocular Depth Estimation","date":"2018-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IcarusWizard/monodepth2-paddle","path":"utils.py","file_url":"https://github.com/IcarusWizard/monodepth2-paddle/blob/HEAD/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":"8749078cf4be8f13","mcp_get_code":{"code_sha256":"8749078cf4be8f13"}},{"arxiv_id":"1703.05593","paper":"/paper/convolutional-neural-network-architecture-for","title":"Convolutional neural network architecture for geometric matching","date":"2017-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ignacio-rocco/cnngeometric_pytorch","path":"image/normalization.py","file_url":"https://github.com/ignacio-rocco/cnngeometric_pytorch/blob/HEAD/image/normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6afbb61727662c3f","mcp_get_code":{"code_sha256":"6afbb61727662c3f"}},{"arxiv_id":"1611.02200","paper":"/paper/unsupervised-cross-domain-image-generation","title":"Unsupervised Cross-Domain Image Generation","date":"2016-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaonashi-tyc/zi2zi","path":"model/utils.py","file_url":"https://github.com/kaonashi-tyc/zi2zi/blob/HEAD/model/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":"45420560ebfbf987","mcp_get_code":{"code_sha256":"45420560ebfbf987"}},{"arxiv_id":"Zhou_R-MSFM_Recurrent_Multi-Scale_Feature_Modulation_for_Monocular_Depth_Estimating_ICCV_2021_paper","paper":null,"title":"arXiv:Zhou_R-MSFM_Recurrent_Multi-Scale_Feature_Modulation_for_Monocular_Depth_Estimating_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jsczzzk/R-MSFM","path":"utils.py","file_url":"https://github.com/jsczzzk/R-MSFM/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"332a1ab65ab9e5a1","mcp_get_code":{"code_sha256":"332a1ab65ab9e5a1"}}]}