{"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/crop-image","entry":"crop_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":17,"n_papers_ran":6,"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":16,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":18,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":3,"unverified":11},"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":"2602.16855","paper":"/paper/arxiv-2602-16855","title":"Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"X-PLUG/MobileAgent","path":"Mobile-Agent-v1/MobileAgent/crop.py","file_url":"https://github.com/X-PLUG/MobileAgent/blob/HEAD/Mobile-Agent-v1/MobileAgent/crop.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b371593bf6e485b9","mcp_get_code":{"code_sha256":"b371593bf6e485b9"}},{"arxiv_id":"2409.20353","paper":"/paper/cableinspect-ad-an-expert-annotated-anomaly","title":"CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mila-iqia/cableinspect-ad-code","path":"dataset/raw_to_cropped_dataset.py","file_url":"https://github.com/mila-iqia/cableinspect-ad-code/blob/HEAD/dataset/raw_to_cropped_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d7f4146dfedb7f98","mcp_get_code":{"code_sha256":"d7f4146dfedb7f98"}},{"arxiv_id":"2407.14816","paper":"/paper/blind-image-deconvolution-by-generative-based","title":"Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding","date":"2024-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jtaoz/gkpile-deconvolution","path":"utils/common_utils.py","file_url":"https://github.com/jtaoz/gkpile-deconvolution/blob/HEAD/utils/common_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fc5ecca14cb070cc","mcp_get_code":{"code_sha256":"fc5ecca14cb070cc"}},{"arxiv_id":"2403.05135","paper":"/paper/ella-equip-diffusion-models-with-llm-for","title":"ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencentqqgylab/ella","path":"dpg_bench/compute_dpg_bench.py","file_url":"https://github.com/tencentqqgylab/ella/blob/HEAD/dpg_bench/compute_dpg_bench.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":"10dc0d8ce3bf1721","mcp_get_code":{"code_sha256":"10dc0d8ce3bf1721"}},{"arxiv_id":"2310.10640","paper":"/paper/llm-blueprint-enabling-text-to-image","title":"LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hananshafi/llmblueprint","path":"composition_module/my_paint_by_example.py","file_url":"https://github.com/hananshafi/llmblueprint/blob/HEAD/composition_module/my_paint_by_example.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cf4b29877ff87f73","mcp_get_code":{"code_sha256":"cf4b29877ff87f73"}},{"arxiv_id":"2309.03244","paper":"/paper/egic-enhanced-low-bit-rate-generative-image","title":"EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation","date":"2023-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nikolai10/SwinT-ChARM","path":"zyc2022.py","file_url":"https://github.com/Nikolai10/SwinT-ChARM/blob/HEAD/zyc2022.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":"aacb725a47e91ac8","mcp_get_code":{"code_sha256":"aacb725a47e91ac8"}},{"arxiv_id":"2212.13824","paper":"/paper/multi-realism-image-compression-with-a","title":"Multi-Realism Image Compression with a Conditional Generator","date":"2022-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nikolai10/MRIC","path":"src/amtm2023.py","file_url":"https://github.com/Nikolai10/MRIC/blob/HEAD/src/amtm2023.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":"aacb725a47e91ac8","mcp_get_code":{"code_sha256":"aacb725a47e91ac8"}},{"arxiv_id":"2110.07604","paper":"/paper/ners-neural-reflectance-surfaces-for-sparse","title":"NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jasonyzhang/ners","path":"ners/utils/image.py","file_url":"https://github.com/jasonyzhang/ners/blob/HEAD/ners/utils/image.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"c7b71b76c6df1fa6","mcp_get_code":{"code_sha256":"c7b71b76c6df1fa6"}},{"arxiv_id":"2003.08685","paper":"/paper/leveraging-frequency-analysis-for-deep-fake","title":"Leveraging Frequency Analysis for Deep Fake Image Recognition","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RUB-SysSec/GANDCTAnalysis","path":"crop_celeba.py","file_url":"https://github.com/RUB-SysSec/GANDCTAnalysis/blob/HEAD/crop_celeba.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de3819ddb7821c62","mcp_get_code":{"code_sha256":"de3819ddb7821c62"}},{"arxiv_id":"1910.11030","paper":"/paper/spatiotemporal-tile-based-attention-guided","title":"Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction","date":"2019-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tumeteor/neurips2019challenge","path":"src/data_utils/loader.py","file_url":"https://github.com/tumeteor/neurips2019challenge/blob/HEAD/src/data_utils/loader.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":"d111ccdd6d16c5ef","mcp_get_code":{"code_sha256":"d111ccdd6d16c5ef"}},{"arxiv_id":"1905.05754","paper":"/paper/190505754","title":"Learnable Triangulation of Human Pose","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karfly/learnable-triangulation-pytorch","path":"mvn/utils/img.py","file_url":"https://github.com/karfly/learnable-triangulation-pytorch/blob/HEAD/mvn/utils/img.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97259a9e1f81bfef","mcp_get_code":{"code_sha256":"97259a9e1f81bfef"}},{"arxiv_id":"1904.02639","paper":"/paper/memorizing-normality-to-detect-anomaly-memory","title":"Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection","date":"2019-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"donggong1/memae-anomaly-detection","path":"utils/utils.py","file_url":"https://github.com/donggong1/memae-anomaly-detection/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbb1ce6b9400bd85","mcp_get_code":{"code_sha256":"dbb1ce6b9400bd85"}},{"arxiv_id":"1902.04502","paper":"/paper/fast-scnn-fast-semantic-segmentation-network","title":"Fast-SCNN: Fast Semantic Segmentation Network","date":"2019-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbaofd/solar-panels-detection","path":"data_processing/data_processing_tool_1.py","file_url":"https://github.com/dbaofd/solar-panels-detection/blob/HEAD/data_processing/data_processing_tool_1.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa7eb507337ce943","mcp_get_code":{"code_sha256":"fa7eb507337ce943"}},{"arxiv_id":"1902.04502","paper":"/paper/fast-scnn-fast-semantic-segmentation-network","title":"Fast-SCNN: Fast Semantic Segmentation Network","date":"2019-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbaofd/solar-panels-detection","path":"data_processing/data_processing_tool_2.py","file_url":"https://github.com/dbaofd/solar-panels-detection/blob/HEAD/data_processing/data_processing_tool_2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01bf9e1fd4ec484d","mcp_get_code":{"code_sha256":"01bf9e1fd4ec484d"}},{"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":"aditbiswas1/P3-behavioral-cloning","path":"drive.py","file_url":"https://github.com/aditbiswas1/P3-behavioral-cloning/blob/HEAD/drive.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e827bbc77666d2d","mcp_get_code":{"code_sha256":"7e827bbc77666d2d"}},{"arxiv_id":"1411.4555","paper":"/paper/show-and-tell-a-neural-image-caption","title":"Show and Tell: A Neural Image Caption Generator","date":"2014-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jazzsaxmafia/show_and_tell.tensorflow","path":"cnn_util.py","file_url":"https://github.com/jazzsaxmafia/show_and_tell.tensorflow/blob/HEAD/cnn_util.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":"e222ea9e1518266f","mcp_get_code":{"code_sha256":"e222ea9e1518266f"}},{"arxiv_id":"aaai_28342","paper":null,"title":"arXiv:aaai_28342","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VUT-HFUT/EulerMormer","path":"utils/inference_process.py","file_url":"https://github.com/VUT-HFUT/EulerMormer/blob/HEAD/utils/inference_process.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b09e7aa1a344faf0","mcp_get_code":{"code_sha256":"b09e7aa1a344faf0"}},{"arxiv_id":"Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper","paper":null,"title":"arXiv:Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Chilie/Deblur_MCEM","path":"utils/common_utils.py","file_url":"https://github.com/Chilie/Deblur_MCEM/blob/HEAD/utils/common_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fc5ecca14cb070cc","mcp_get_code":{"code_sha256":"fc5ecca14cb070cc"}}]}