{"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/positionembeddingsine","entry":"PositionEmbeddingSine","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":21,"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":22,"n_samples_fingerprinted":13,"n_places":22,"n_places_pointer_only":10,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":22,"unverified":0},"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":"2606.19185","paper":"/paper/arxiv-2606-19185","title":"AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"LabRAI/AGDN","path":"supervised/models/agd.py","file_url":"https://github.com/LabRAI/AGDN/blob/HEAD/supervised/models/agd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"20102ab800db2eb0","mcp_get_code":{"code_sha256":"20102ab800db2eb0"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7866976ca082413e","mcp_get_code":{"code_sha256":"7866976ca082413e"}},{"arxiv_id":"2510.00495","paper":"/paper/arxiv-2510-00495","title":"Normal-Abnormal Guided Generalist Anomaly Detection","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"JasonKyng/NAGL","path":"models/model.py","file_url":"https://github.com/JasonKyng/NAGL/blob/HEAD/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f02c425d460a256","mcp_get_code":{"code_sha256":"7f02c425d460a256"}},{"arxiv_id":"2505.18686","paper":"/paper/weakmcn-multi-task-collaborative-network-for","title":"WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation","date":"2025-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MRUIL/WeakMCN","path":"models/weakmcn/net.py","file_url":"https://github.com/MRUIL/WeakMCN/blob/HEAD/models/weakmcn/net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"02042439d613744f","mcp_get_code":{"code_sha256":"02042439d613744f"}},{"arxiv_id":"2503.09402","paper":"/paper/vlog-video-language-models-by-generative","title":"VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary","date":"2025-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/VLog","path":"VLog/model/models.py","file_url":"https://github.com/showlab/VLog/blob/HEAD/VLog/model/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"77fa2643a3a1c67a","mcp_get_code":{"code_sha256":"77fa2643a3a1c67a"}},{"arxiv_id":"2501.02464","paper":"/paper/depth-any-camera-zero-shot-metric-depth","title":"Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera","date":"2025-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuliangguo/depth_any_camera","path":"dac/models/idisc.py","file_url":"https://github.com/yuliangguo/depth_any_camera/blob/HEAD/dac/models/idisc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e19a81e9ad95806e","mcp_get_code":{"code_sha256":"e19a81e9ad95806e"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6738d0ca7aecfc9","mcp_get_code":{"code_sha256":"f6738d0ca7aecfc9"}},{"arxiv_id":"2405.16273","paper":"/paper/m-3-gpt-an-advanced-multimodal-multitask","title":"M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luomingshuang/m3gpt","path":"m3gpt/core/models/decoders/network/transformer_decoder/transformer_decoder.py","file_url":"https://github.com/luomingshuang/m3gpt/blob/HEAD/m3gpt/core/models/decoders/network/transformer_decoder/transformer_decoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cb65f982177791b7","mcp_get_code":{"code_sha256":"cb65f982177791b7"}},{"arxiv_id":"2402.17726","paper":"/paper/vrp-sam-sam-with-visual-reference-prompt","title":"VRP-SAM: SAM with Visual Reference Prompt","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syp2ysy/VRP-SAM","path":"model/VRP_encoder.py","file_url":"https://github.com/syp2ysy/VRP-SAM/blob/HEAD/model/VRP_encoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"865b58f1322f66eb","mcp_get_code":{"code_sha256":"865b58f1322f66eb"}},{"arxiv_id":"2308.13814","paper":"/paper/point-query-quadtree-for-crowd-counting","title":"Point-Query Quadtree for Crowd Counting, Localization, and More","date":"2023-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cxliu0/PET","path":"models/pet.py","file_url":"https://github.com/cxliu0/PET/blob/HEAD/models/pet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6cf4af72cee273fc","mcp_get_code":{"code_sha256":"6cf4af72cee273fc"}},{"arxiv_id":"2304.10131","paper":"/paper/learning-bottleneck-concepts-in-image","title":"Learning Bottleneck Concepts in Image Classification","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wbw520/botcl","path":"model/reconstruct/model_main.py","file_url":"https://github.com/wbw520/botcl/blob/HEAD/model/reconstruct/model_main.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac35c5d9fb6e692c","mcp_get_code":{"code_sha256":"ac35c5d9fb6e692c"}},{"arxiv_id":"2304.06334","paper":"/paper/idisc-internal-discretization-for-monocular","title":"iDisc: Internal Discretization for Monocular Depth Estimation","date":"2023-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SysCV/idisc","path":"idisc/models/id_module.py","file_url":"https://github.com/SysCV/idisc/blob/HEAD/idisc/models/id_module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"31f137df1d61458e","mcp_get_code":{"code_sha256":"31f137df1d61458e"}},{"arxiv_id":"2209.13306","paper":"/paper/embracing-consistency-a-one-stage-approach","title":"Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding","date":"2022-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jy0205/stcat","path":"models/pipeline.py","file_url":"https://github.com/jy0205/stcat/blob/HEAD/models/pipeline.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":"f52bb0cf00c7d60c","mcp_get_code":{"code_sha256":"f52bb0cf00c7d60c"}},{"arxiv_id":"2209.02242","paper":"/paper/ptseformer-progressive-temporal-spatial","title":"PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection","date":"2022-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hon-Wong/PTSEFormer","path":"src/models/model_builder.py","file_url":"https://github.com/Hon-Wong/PTSEFormer/blob/HEAD/src/models/model_builder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"747992503b5e8c6b","mcp_get_code":{"code_sha256":"747992503b5e8c6b"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab7c9f19268bf5b1","mcp_get_code":{"code_sha256":"ab7c9f19268bf5b1"}},{"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":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4ca9a91816f9d68","mcp_get_code":{"code_sha256":"b4ca9a91816f9d68"}},{"arxiv_id":"2207.08677","paper":"/paper/label2label-a-language-modeling-framework-for","title":"Label2Label: A Language Modeling Framework for Multi-Attribute Learning","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Li-Wanhua/Label2Label","path":"Face_Attribute/model.py","file_url":"https://github.com/Li-Wanhua/Label2Label/blob/HEAD/Face_Attribute/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13d57cd5c6586dc6","mcp_get_code":{"code_sha256":"13d57cd5c6586dc6"}},{"arxiv_id":"2206.03687","paper":"/paper/a-unified-model-for-multi-class-anomaly","title":"A Unified Model for Multi-class Anomaly Detection","date":"2022-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiyuanyou/uniad","path":"models/reconstructions/uniad.py","file_url":"https://github.com/zhiyuanyou/uniad/blob/HEAD/models/reconstructions/uniad.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c74afaad75da67ca","mcp_get_code":{"code_sha256":"c74afaad75da67ca"}},{"arxiv_id":"2110.04722","paper":"/paper/transformer-based-dual-relation-graph-for-1","title":"Transformer-based Dual Relation Graph for Multi-label Image Recognition","date":"2021-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iCVTEAM/TDRG","path":"models/TDRG.py","file_url":"https://github.com/iCVTEAM/TDRG/blob/HEAD/models/TDRG.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":"19ad134c39cefe3a","mcp_get_code":{"code_sha256":"19ad134c39cefe3a"}},{"arxiv_id":"2103.11161","paper":"/paper/montefloor-extending-mcts-for-reconstructing","title":"MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans","date":"2021-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"woodfrog/poly-diffuse","path":"src/models/polygon_models/polygon_net.py","file_url":"https://github.com/woodfrog/poly-diffuse/blob/HEAD/src/models/polygon_models/polygon_net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"f021c121c7024822","mcp_get_code":{"code_sha256":"f021c121c7024822"}},{"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":"Leonardo-Blanger/detr_tensorflow","path":"detr_tensorflow/models/detr.py","file_url":"https://github.com/Leonardo-Blanger/detr_tensorflow/blob/HEAD/detr_tensorflow/models/detr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30bbae6bd017a72b","mcp_get_code":{"code_sha256":"30bbae6bd017a72b"}},{"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":"clive819/Modified-DETR","path":"models/detr.py","file_url":"https://github.com/clive819/Modified-DETR/blob/HEAD/models/detr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3f7df5ae93d75f73","mcp_get_code":{"code_sha256":"3f7df5ae93d75f73"}}]}