{"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/get-activation-fn-2","entry":"_get_activation_fn","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":21,"n_papers_ran":20,"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":21,"n_samples_ran":20,"n_samples_fingerprinted":0,"n_places":21,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":20,"ran_fixture":0,"ran":0,"unverified":1},"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":"2602.00635","paper":"/paper/arxiv-2602-00635","title":"S 3 POT: Contrast-Driven Face Occlusion Segmentation via Self-Supervised Prompt Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Bh-Johnny/S3SPOT","path":"model/Transformer_decoder.py","file_url":"https://github.com/Bh-Johnny/S3SPOT/blob/HEAD/model/Transformer_decoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad74da3f2efb5b95","mcp_get_code":{"code_sha256":"ad74da3f2efb5b95"}},{"arxiv_id":"2412.07236","paper":"/paper/cbramod-a-criss-cross-brain-foundation-model","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjq-learning/cbramod","path":"models/cbramod.py","file_url":"https://github.com/wjq-learning/cbramod/blob/HEAD/models/cbramod.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6dc6173b618ea485","mcp_get_code":{"code_sha256":"6dc6173b618ea485"}},{"arxiv_id":"2410.05016","paper":"/paper/t-jepa-augmentation-free-self-supervised","title":"T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jose-melo/t-jepa","path":"src/encoder.py","file_url":"https://github.com/jose-melo/t-jepa/blob/HEAD/src/encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7372b44b96fac487","mcp_get_code":{"code_sha256":"7372b44b96fac487"}},{"arxiv_id":"2407.03200","paper":"/paper/segvg-transferring-object-bounding-box-to","title":"SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding","date":"2024-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WeitaiKang/SegVG","path":"models/SegVG.py","file_url":"https://github.com/WeitaiKang/SegVG/blob/HEAD/models/SegVG.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0bb264f52e707e08","mcp_get_code":{"code_sha256":"0bb264f52e707e08"}},{"arxiv_id":"2402.04754","paper":"/paper/towards-aligned-layout-generation-via","title":"Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"puar-playground/lace","path":"model_diffusion.py","file_url":"https://github.com/puar-playground/lace/blob/HEAD/model_diffusion.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f6ff9fa607114db","mcp_get_code":{"code_sha256":"1f6ff9fa607114db"}},{"arxiv_id":"2309.16319","paper":"/paper/augmenting-transformers-with-recursively","title":"Augmenting Transformers with Recursively Composed Multi-grained Representations","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/structuredlm_rtdt","path":"model/tree_encoder.py","file_url":"https://github.com/ant-research/structuredlm_rtdt/blob/HEAD/model/tree_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a9d317f927330280","mcp_get_code":{"code_sha256":"a9d317f927330280"}},{"arxiv_id":"2304.04997","paper":"/paper/relational-context-learning-for-human-object","title":"Relational Context Learning for Human-Object Interaction Detection","date":"2023-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OreoChocolate/MUREN","path":"models/muren.py","file_url":"https://github.com/OreoChocolate/MUREN/blob/HEAD/models/muren.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1fe4b00569a0d192","mcp_get_code":{"code_sha256":"1fe4b00569a0d192"}},{"arxiv_id":"2303.07335","paper":"/paper/lite-detr-an-interleaved-multi-scale-encoder","title":"Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDEA-Research/Lite-DETR","path":"models/dino/deformable_transformer.py","file_url":"https://github.com/IDEA-Research/Lite-DETR/blob/HEAD/models/dino/deformable_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80168c1aef8576f9","mcp_get_code":{"code_sha256":"80168c1aef8576f9"}},{"arxiv_id":"2207.13820","paper":"/paper/cross-attention-of-disentangled-modalities","title":"Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"postech-ami/fastmetro","path":"src/modeling/model/modeling_fastmetro.py","file_url":"https://github.com/postech-ami/fastmetro/blob/HEAD/src/modeling/model/modeling_fastmetro.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d493e14665ada3f2","mcp_get_code":{"code_sha256":"d493e14665ada3f2"}},{"arxiv_id":"2207.10273","paper":"/paper/don-t-forget-me-accurate-background-recovery","title":"Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context","date":"2022-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lcy0604/CTRNet","path":"models_CTRNet.py","file_url":"https://github.com/lcy0604/CTRNet/blob/HEAD/models_CTRNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b38b74ab50f7c296","mcp_get_code":{"code_sha256":"b38b74ab50f7c296"}},{"arxiv_id":"2206.03789","paper":"/paper/language-bridged-spatial-temporal-interaction-1","title":"Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation","date":"2022-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dzh19990407/lbdt","path":"models/LBDT_4.py","file_url":"https://github.com/dzh19990407/lbdt/blob/HEAD/models/LBDT_4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ccef9d29ffdd9c9","mcp_get_code":{"code_sha256":"1ccef9d29ffdd9c9"}},{"arxiv_id":"2205.09328","paper":"/paper/transtab-learning-transferable-tabular","title":"TransTab: Learning Transferable Tabular Transformers Across Tables","date":"2022-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanwangzf/transtab","path":"transtab/modeling_transtab.py","file_url":"https://github.com/ryanwangzf/transtab/blob/HEAD/transtab/modeling_transtab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"5189f4bea3053264","mcp_get_code":{"code_sha256":"5189f4bea3053264"}},{"arxiv_id":"2203.08459","paper":"/paper/kinyabert-a-morphology-aware-kinyarwanda-1","title":"KinyaBERT: a Morphology-aware Kinyarwanda Language Model","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anzeyimana/kinyabert-acl2022","path":"code/morpho_model.py","file_url":"https://github.com/anzeyimana/kinyabert-acl2022/blob/HEAD/code/morpho_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b97233bc0d62d1ca","mcp_get_code":{"code_sha256":"b97233bc0d62d1ca"}},{"arxiv_id":"2106.08417","paper":"/paper/scene-transformer-a-unified-multi-task-model","title":"Scene Transformer: A unified architecture for predicting multiple agent trajectories","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhejz/trafficbotsv1.5","path":"src/models/modules/transformer_rpe.py","file_url":"https://github.com/zhejz/trafficbotsv1.5/blob/HEAD/src/models/modules/transformer_rpe.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ef1d31b5e6915b9e","mcp_get_code":{"code_sha256":"ef1d31b5e6915b9e"}},{"arxiv_id":"2105.11601","paper":"/paper/personalized-transformer-for-explainable","title":"Personalized Transformer for Explainable Recommendation","date":"2021-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lileipisces/PETER","path":"module.py","file_url":"https://github.com/lileipisces/PETER/blob/HEAD/module.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"df8238828085971b","mcp_get_code":{"code_sha256":"df8238828085971b"}},{"arxiv_id":"2103.14146","paper":"/paper/describing-and-localizing-multiple-changes","title":"Describing and Localizing Multiple Changes with Transformers","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"doiken23/mccformers.pytorch","path":"models/model.py","file_url":"https://github.com/doiken23/mccformers.pytorch/blob/HEAD/models/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25f24e4d230ced80","mcp_get_code":{"code_sha256":"25f24e4d230ced80"}},{"arxiv_id":"2103.03027","paper":"/paper/modeling-multi-label-action-dependencies-for","title":"Modeling Multi-Label Action Dependencies for Temporal Action Localization","date":"2021-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ptirupat/MLAD","path":"src/models/v1.py","file_url":"https://github.com/ptirupat/MLAD/blob/HEAD/src/models/v1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"611814a69b40fe4b","mcp_get_code":{"code_sha256":"611814a69b40fe4b"}},{"arxiv_id":"2010.04159","paper":"/paper/deformable-detr-deformable-transformers-for-1","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","date":"2020-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LONGXUANX/CDFormer_code","path":"models/deformable_transformer.py","file_url":"https://github.com/LONGXUANX/CDFormer_code/blob/HEAD/models/deformable_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c050b2542997c417","mcp_get_code":{"code_sha256":"c050b2542997c417"}},{"arxiv_id":"2009.08128","paper":"/paper/multi-2oie-multilingual-open-information","title":"Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT","date":"2020-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"youngbin-ro/Multi2OIE","path":"model.py","file_url":"https://github.com/youngbin-ro/Multi2OIE/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc650062db9ad0fd","mcp_get_code":{"code_sha256":"cc650062db9ad0fd"}},{"arxiv_id":"2005.12872","paper":"/paper/end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/EasyCV","path":"easycv/models/detection/detectors/detr/detr_transformer.py","file_url":"https://github.com/alibaba/EasyCV/blob/HEAD/easycv/models/detection/detectors/detr/detr_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"184de768ec89ba57","mcp_get_code":{"code_sha256":"184de768ec89ba57"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JanAthmer/Compositional-generalization-capabillity-of-Transformer","path":"models/transformer.py","file_url":"https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer/blob/HEAD/models/transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7dd81daf6a1e96ce","mcp_get_code":{"code_sha256":"7dd81daf6a1e96ce"}}]}