{"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/transform-image","entry":"transform_image","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":7,"n_papers_ran":0,"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":7,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":7},"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":"2304.10597","paper":"/paper/text2seg-remote-sensing-image-semantic","title":"Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"douglas2code/text2seg","path":"text2seg/utils.py","file_url":"https://github.com/douglas2code/text2seg/blob/HEAD/text2seg/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb922109681f9d66","mcp_get_code":{"code_sha256":"eb922109681f9d66"}},{"arxiv_id":"2211.10681","paper":"/paper/decomposed-soft-prompt-guided-fusion","title":"Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning","date":"2022-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"forest-art/dfsp","path":"dataset.py","file_url":"https://github.com/forest-art/dfsp/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c0fa8be4df58ae0","mcp_get_code":{"code_sha256":"2c0fa8be4df58ae0"}},{"arxiv_id":"2210.01738","paper":"/paper/asif-coupled-data-turns-unimodal-models-to-1","title":"ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noranta4/asif","path":"relrepsutils.py","file_url":"https://github.com/noranta4/asif/blob/HEAD/relrepsutils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9dc15dd5fec40d34","mcp_get_code":{"code_sha256":"9dc15dd5fec40d34"}},{"arxiv_id":"2203.03818","paper":"/paper/shadows-can-be-dangerous-stealthy-and","title":"Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hncszyq/ShadowAttack","path":"gtsrb.py","file_url":"https://github.com/hncszyq/ShadowAttack/blob/HEAD/gtsrb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a028dbff86b13d5","mcp_get_code":{"code_sha256":"4a028dbff86b13d5"}},{"arxiv_id":"1807.09856","paper":"/paper/where-are-the-blobs-counting-by-localization","title":"Where are the Blobs: Counting by Localization with Point Supervision","date":"2018-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ElementAI/LCFCN","path":"lcfcn/lcfcn.py","file_url":"https://github.com/ElementAI/LCFCN/blob/HEAD/lcfcn/lcfcn.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":"ec5e6a234f9b03db","mcp_get_code":{"code_sha256":"ec5e6a234f9b03db"}},{"arxiv_id":"1802.04799","paper":"/paper/tvm-an-automated-end-to-end-optimizing","title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning","date":"2018-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ctuning/ck-tvm","path":"program/image-classification-tvm/classify.py","file_url":"https://github.com/ctuning/ck-tvm/blob/HEAD/program/image-classification-tvm/classify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"e7267f02e6660d86","mcp_get_code":{"code_sha256":"e7267f02e6660d86"}},{"arxiv_id":"Lemmer_Ground-Truth_or_DAER_Selective_Re-Query_of_Secondary_Information_ICCV_2021_paper","paper":null,"title":"arXiv:Lemmer_Ground-Truth_or_DAER_Selective_Re-Query_of_Secondary_Information_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lemmersj/ground-truth-or-daer","path":"kcve/make_heatmaps_from_reject_model.py","file_url":"https://github.com/lemmersj/ground-truth-or-daer/blob/HEAD/kcve/make_heatmaps_from_reject_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d1384c519636931","mcp_get_code":{"code_sha256":"6d1384c519636931"}}]}