{"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/backwarp","entry":"backwarp","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":5,"n_papers_ran":4,"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":5,"n_samples_ran":4,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":2,"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":"2403.03662","paper":"/paper/harnessing-meta-learning-for-improving-full","title":"Harnessing Meta-Learning for Improving Full-Frame Video Stabilization","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mkashifali/metavideostab","path":"PWC_Module/PWC_Module.py","file_url":"https://github.com/mkashifali/metavideostab/blob/HEAD/PWC_Module/PWC_Module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9b4c98ad5c94b943","mcp_get_code":{"code_sha256":"9b4c98ad5c94b943"}},{"arxiv_id":"2309.16217","paper":"/paper/gaflow-incorporating-gaussian-attention-into-1","title":"GAFlow: Incorporating Gaussian Attention into Optical Flow","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LA30/GAFlow","path":"core/model/loss_gmflownet.py","file_url":"https://github.com/LA30/GAFlow/blob/HEAD/core/model/loss_gmflownet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"798a2174fc62b871","mcp_get_code":{"code_sha256":"798a2174fc62b871"}},{"arxiv_id":"2308.13133","paper":"/paper/accflow-backward-accumulation-for-long-range","title":"AccFlow: Backward Accumulation for Long-Range Optical Flow","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mulns/AccFlow","path":"networks/AccFlow_.py","file_url":"https://github.com/mulns/AccFlow/blob/HEAD/networks/AccFlow_.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e854d3d7f3980c8","mcp_get_code":{"code_sha256":"6e854d3d7f3980c8"}},{"arxiv_id":"2212.05342","paper":"/paper/benchmark-dataset-and-effective-inter-frame","title":"Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution","date":"2022-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitrainer/eavsr","path":"models/spy_net.py","file_url":"https://github.com/hitrainer/eavsr/blob/HEAD/models/spy_net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed5bf923e379575","mcp_get_code":{"code_sha256":"aed5bf923e379575"}},{"arxiv_id":"1911.00627","paper":"/paper/quadratic-video-interpolation","title":"Quadratic video interpolation","date":"2019-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuxy09/QVI","path":"models/QVI.py","file_url":"https://github.com/xuxy09/QVI/blob/HEAD/models/QVI.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c0760691fa1c116a","mcp_get_code":{"code_sha256":"c0760691fa1c116a"}}]}