{"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/generalized-box-iou","entry":"generalized_box_iou","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":8,"n_samples_ran":7,"n_samples_fingerprinted":7,"n_places":11,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":5,"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":"2505.20753","paper":"/paper/understand-think-and-answer-advancing-visual","title":"Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models","date":"2025-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jefferyzhan/griffon","path":"griffon/coor_utils.py","file_url":"https://github.com/jefferyzhan/griffon/blob/HEAD/griffon/coor_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dc8fb9b6fb48ac37","mcp_get_code":{"code_sha256":"dc8fb9b6fb48ac37"}},{"arxiv_id":"2410.13842","paper":"/paper/d-fine-redefine-regression-task-in-detrs-as","title":"D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Peterande/D-FINE","path":"src/misc/box_ops.py","file_url":"https://github.com/Peterande/D-FINE/blob/HEAD/src/misc/box_ops.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2f52077940b53c42","mcp_get_code":{"code_sha256":"2f52077940b53c42"}},{"arxiv_id":"2404.08506","paper":"/paper/lasagna-language-based-segmentation-assistant","title":"LaSagnA: Language-based Segmentation Assistant for Complex Queries","date":"2024-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"congvvc/lasagna","path":"model/matcher.py","file_url":"https://github.com/congvvc/lasagna/blob/HEAD/model/matcher.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dc8fb9b6fb48ac37","mcp_get_code":{"code_sha256":"dc8fb9b6fb48ac37"}},{"arxiv_id":"2403.06213","paper":"/paper/v-kd-improving-knowledge-distillation-using","title":"$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"roymiles/vkd","path":"vidt/methods/vidt/criterion.py","file_url":"https://github.com/roymiles/vkd/blob/HEAD/vidt/methods/vidt/criterion.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dc8fb9b6fb48ac37","mcp_get_code":{"code_sha256":"dc8fb9b6fb48ac37"}},{"arxiv_id":"2311.05348","paper":"/paper/u-llava-unifying-multi-modal-tasks-via-large","title":"u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OPPOMKLab/u-LLaVA","path":"models/loss.py","file_url":"https://github.com/OPPOMKLab/u-LLaVA/blob/HEAD/models/loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dc8fb9b6fb48ac37","mcp_get_code":{"code_sha256":"dc8fb9b6fb48ac37"}},{"arxiv_id":"2309.12969","paper":"/paper/detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlzxy/devit","path":"detectron2/modeling/meta_arch/devit_update.py","file_url":"https://github.com/mlzxy/devit/blob/HEAD/detectron2/modeling/meta_arch/devit_update.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"761e870ed51af37c","mcp_get_code":{"code_sha256":"761e870ed51af37c"}},{"arxiv_id":"2309.12969","paper":"/paper/detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlzxy/devit","path":"detectron2/modeling/meta_arch/devit.py","file_url":"https://github.com/mlzxy/devit/blob/HEAD/detectron2/modeling/meta_arch/devit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bfc4e32519b5ef25","mcp_get_code":{"code_sha256":"bfc4e32519b5ef25"}},{"arxiv_id":"2304.04742","paper":"/paper/detection-transformer-with-stable-matching","title":"Detection Transformer with Stable Matching","date":"2023-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDEA-Research/detrex","path":"detrex/modeling/matcher/modified_matcher.py","file_url":"https://github.com/IDEA-Research/detrex/blob/HEAD/detrex/modeling/matcher/modified_matcher.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"be174c5da2b1ca9a","mcp_get_code":{"code_sha256":"be174c5da2b1ca9a"}},{"arxiv_id":"2303.14404","paper":"/paper/bridging-precision-and-confidence-a-train","title":"Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akhtarvision/bpc_calibration","path":"models/deformable_detr.py","file_url":"https://github.com/akhtarvision/bpc_calibration/blob/HEAD/models/deformable_detr.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6097d6378e7d3046","mcp_get_code":{"code_sha256":"6097d6378e7d3046"}},{"arxiv_id":"2207.10661","paper":"/paper/in-defense-of-online-models-for-video","title":"In Defense of Online Models for Video Instance Segmentation","date":"2022-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mkimhi/RISE","path":"projects/RISE/rise/models/pos_neg_select.py","file_url":"https://github.com/mkimhi/RISE/blob/HEAD/projects/RISE/rise/models/pos_neg_select.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d0ce32748ce099f4","mcp_get_code":{"code_sha256":"d0ce32748ce099f4"}},{"arxiv_id":"2103.14259","paper":"/paper/ota-optimal-transport-assignment-for-object","title":"OTA: Optimal Transport Assignment for Object Detection","date":"2021-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CV51GO/OTA_Megengine","path":"official_OTA/cvpods/cvpods/modeling/matcher.py","file_url":"https://github.com/CV51GO/OTA_Megengine/blob/HEAD/official_OTA/cvpods/cvpods/modeling/matcher.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"32254c2bce32284c","mcp_get_code":{"code_sha256":"32254c2bce32284c"}}]}