{"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/modifiedresnet","entry":"ModifiedResNet","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":15,"n_papers_ran":9,"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":15,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":9,"unverified":6},"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":"2510.20162","paper":"/paper/arxiv-2510-20162","title":"TOMCAT : Test-time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"xud-yan/TOMCAT","path":"model/tomcat_bm.py","file_url":"https://github.com/xud-yan/TOMCAT/blob/HEAD/model/tomcat_bm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"39ac004ae4ac2651","mcp_get_code":{"code_sha256":"39ac004ae4ac2651"}},{"arxiv_id":"2510.18583","paper":"/paper/arxiv-2510-18583","title":"CovMatch: Cross-Covariance Guided Multimodal Dataset Distillation with Trainable Text Encoder","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Yongalls/CovMatch","path":"src/model.py","file_url":"https://github.com/Yongalls/CovMatch/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aedc8261aa63cda3","mcp_get_code":{"code_sha256":"aedc8261aa63cda3"}},{"arxiv_id":"2505.10289","paper":"/paper/msci-addressing-clip-s-inherent-limitations","title":"MSCI: Addressing CLIP's Inherent Limitations for Compositional Zero-Shot Learning","date":"2025-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltpwy/MSCI","path":"MSCI/code/model/Mutifuse_new.py","file_url":"https://github.com/ltpwy/MSCI/blob/HEAD/MSCI/code/model/Mutifuse_new.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"88fd2d47fac54f60","mcp_get_code":{"code_sha256":"88fd2d47fac54f60"}},{"arxiv_id":"2504.12104","paper":"/paper/logits-deconfusion-with-clip-for-few-shot","title":"Logits DeConfusion with CLIP for Few-Shot Learning","date":"2025-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiShuo1001/LDC","path":"clip_ldc/model.py","file_url":"https://github.com/LiShuo1001/LDC/blob/HEAD/clip_ldc/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2aeba1d2520051c5","mcp_get_code":{"code_sha256":"2aeba1d2520051c5"}},{"arxiv_id":"2411.03313","paper":"/paper/classification-done-right-for-vision-language","title":"Classification Done Right for Vision-Language Pre-Training","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"x-cls/superclass","path":"opencls/open_clip/cls_model.py","file_url":"https://github.com/x-cls/superclass/blob/HEAD/opencls/open_clip/cls_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":"368e98cc03933cb5","mcp_get_code":{"code_sha256":"368e98cc03933cb5"}},{"arxiv_id":"2309.12867","paper":"/paper/accurate-and-fast-compressed-video-captioning","title":"Accurate and Fast Compressed Video Captioning","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acherstyx/CoCap","path":"cocap/modules/compressed_video/compressed_video_transformer.py","file_url":"https://github.com/acherstyx/CoCap/blob/HEAD/cocap/modules/compressed_video/compressed_video_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a558f6e14ab39732","mcp_get_code":{"code_sha256":"a558f6e14ab39732"}},{"arxiv_id":"2308.08428","paper":"/paper/alip-adaptive-language-image-pre-training","title":"ALIP: Adaptive Language-Image Pre-training with Synthetic Caption","date":"2023-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepglint/alip","path":"src/open_alip/model.py","file_url":"https://github.com/deepglint/alip/blob/HEAD/src/open_alip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1bb95acac49af730","mcp_get_code":{"code_sha256":"1bb95acac49af730"}},{"arxiv_id":"2306.09244","paper":"/paper/text-promptable-surgical-instrument","title":"Text Promptable Surgical Instrument Segmentation with Vision-Language Models","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"franciszzj/tp-sis","path":"model/segmenter.py","file_url":"https://github.com/franciszzj/tp-sis/blob/HEAD/model/segmenter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dc896241245fc34","mcp_get_code":{"code_sha256":"4dc896241245fc34"}},{"arxiv_id":"2306.09200","paper":"/paper/chessgpt-bridging-policy-learning-and-1","title":"ChessGPT: Bridging Policy Learning and Language Modeling","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waterhorse1/chessgpt","path":"chessclip/src/open_clip/model.py","file_url":"https://github.com/waterhorse1/chessgpt/blob/HEAD/chessclip/src/open_clip/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":"bd639b02dd5683ff","mcp_get_code":{"code_sha256":"bd639b02dd5683ff"}},{"arxiv_id":"2305.13500","paper":"/paper/learning-emotion-representations-from-verbal-1","title":"Learning Emotion Representations from Verbal and Nonverbal Communication","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xeaver/EmotionCLIP","path":"src/models/base.py","file_url":"https://github.com/Xeaver/EmotionCLIP/blob/HEAD/src/models/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e16cbf2cae362701","mcp_get_code":{"code_sha256":"e16cbf2cae362701"}},{"arxiv_id":"2303.14369","paper":"/paper/video-text-as-game-players-hierarchical","title":"Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jpthu17/dicosa","path":"tvr/models/modeling.py","file_url":"https://github.com/jpthu17/dicosa/blob/HEAD/tvr/models/modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"09e8fef38647435a","mcp_get_code":{"code_sha256":"09e8fef38647435a"}},{"arxiv_id":"2303.12501","paper":"/paper/cross-modal-implicit-relation-reasoning-and","title":"Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval","date":"2023-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anosorae/irra","path":"model/build.py","file_url":"https://github.com/anosorae/irra/blob/HEAD/model/build.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"88e333b1d22a6f6a","mcp_get_code":{"code_sha256":"88e333b1d22a6f6a"}},{"arxiv_id":"2205.14459","paper":"/paper/cyclip-cyclic-contrastive-language-image","title":"CyCLIP: Cyclic Contrastive Language-Image Pretraining","date":"2022-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goel-shashank/CyCLIP","path":"pkgs/openai/model.py","file_url":"https://github.com/goel-shashank/CyCLIP/blob/HEAD/pkgs/openai/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e69131b398a3dfac","mcp_get_code":{"code_sha256":"e69131b398a3dfac"}},{"arxiv_id":"2102.05918","paper":"/paper/scaling-up-visual-and-vision-language","title":"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision","date":"2021-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/metaclip","path":"src/mini_clip/model.py","file_url":"https://github.com/facebookresearch/metaclip/blob/HEAD/src/mini_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6da993cf6b63ba3a","mcp_get_code":{"code_sha256":"6da993cf6b63ba3a"}},{"arxiv_id":"2025.findings-emnlp.28","paper":null,"title":"arXiv:2025.findings-emnlp.28","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"BUAAPY/ProPy","path":"modules/clip_propy.py","file_url":"https://github.com/BUAAPY/ProPy/blob/HEAD/modules/clip_propy.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa27868aaf740f13","mcp_get_code":{"code_sha256":"fa27868aaf740f13"}}]}