{"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/refine","entry":"Refine","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":10,"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":10,"n_samples_ran":10,"n_samples_fingerprinted":2,"n_places":10,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":10,"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":"2404.03635","paper":"/paper/wordepth-variational-language-prior-for","title":"WorDepth: Variational Language Prior for Monocular Depth Estimation","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Adonis-galaxy/WorDepth","path":"src/networks/wordepth.py","file_url":"https://github.com/Adonis-galaxy/WorDepth/blob/HEAD/src/networks/wordepth.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"22c1941ae236e18b","mcp_get_code":{"code_sha256":"22c1941ae236e18b"}},{"arxiv_id":"2402.18217","paper":"/paper/region-aware-exposure-consistency-network-for","title":"Region-Aware Exposure Consistency Network for Mixed Exposure Correction","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kravrolens/RECNet","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3fe57de5f47b21c","mcp_get_code":{"code_sha256":"a3fe57de5f47b21c"}},{"arxiv_id":"2401.02151","paper":"/paper/frequency-adaptive-pan-sharpening-with","title":"Frequency-Adaptive Pan-Sharpening with Mixture of Experts","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexhe101/FAME-Net","path":"model/moe.py","file_url":"https://github.com/alexhe101/FAME-Net/blob/HEAD/model/moe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"489d626b9ca31ffc","mcp_get_code":{"code_sha256":"489d626b9ca31ffc"}},{"arxiv_id":"2308.14153","paper":"/paper/sparse-sampling-transformer-with-uncertainty","title":"Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks","date":"2023-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ephemeral182/UDR-S2Former_deraining","path":"UDR_S2Former.py","file_url":"https://github.com/Ephemeral182/UDR-S2Former_deraining/blob/HEAD/UDR_S2Former.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"808687d27e8153df","mcp_get_code":{"code_sha256":"808687d27e8153df"}},{"arxiv_id":"2109.11404","paper":"/paper/hierarchical-memory-matching-network-for","title":"Hierarchical Memory Matching Network for Video Object Segmentation","date":"2021-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hongje/HMMN","path":"model.py","file_url":"https://github.com/Hongje/HMMN/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0aa276a048959a5a","mcp_get_code":{"code_sha256":"0aa276a048959a5a"}},{"arxiv_id":"2108.08482","paper":"/paper/vil-100-a-new-dataset-and-a-baseline-model","title":"VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection","date":"2021-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yujun0-0/MMA-Net","path":"libs/models/STM.py","file_url":"https://github.com/yujun0-0/MMA-Net/blob/HEAD/libs/models/STM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e0835b53ed7a160","mcp_get_code":{"code_sha256":"0e0835b53ed7a160"}},{"arxiv_id":"2104.10386","paper":"/paper/guided-interactive-video-object-segmentation","title":"Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps","date":"2021-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuk6heo/GIS-RAmap","path":"networks/network.py","file_url":"https://github.com/yuk6heo/GIS-RAmap/blob/HEAD/networks/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3041bd5ec9f44211","mcp_get_code":{"code_sha256":"3041bd5ec9f44211"}},{"arxiv_id":"2103.13746","paper":"/paper/video-instance-segmentation-with-a-propose","title":"Video Instance Segmentation with a Propose-Reduce Paradigm","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvlab-research/proposereduce","path":"mmdet/models/prop_heads/seq_prop_head.py","file_url":"https://github.com/dvlab-research/proposereduce/blob/HEAD/mmdet/models/prop_heads/seq_prop_head.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e8ca60fbece7dea","mcp_get_code":{"code_sha256":"7e8ca60fbece7dea"}},{"arxiv_id":"2103.12934","paper":"/paper/efficient-regional-memory-network-for-video","title":"Efficient Regional Memory Network for Video Object Segmentation","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzxie/RMNet","path":"models/rmnet.py","file_url":"https://github.com/hzxie/RMNet/blob/HEAD/models/rmnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"47c819e0ff62ea4b","mcp_get_code":{"code_sha256":"47c819e0ff62ea4b"}},{"arxiv_id":"2102.04604","paper":"/paper/swiftnet-real-time-video-object-segmentation","title":"SwiftNet: Real-time Video Object Segmentation","date":"2021-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haochenheheda/SwiftNet","path":"swiftnet.py","file_url":"https://github.com/haochenheheda/SwiftNet/blob/HEAD/swiftnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7a2451763dfa3a42","mcp_get_code":{"code_sha256":"7a2451763dfa3a42"}}]}