{"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/resize-images","entry":"resize_images","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":9,"n_papers_ran":2,"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":9,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2601.14951","paper":"/paper/arxiv-2601-14951","title":"TEMPVIZ: On the Evaluation of Temporal Knowledge in Text-to-Image Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"TAI-HAMBURG/TempViz","path":"code/prompt_vlm_models.py","file_url":"https://github.com/TAI-HAMBURG/TempViz/blob/HEAD/code/prompt_vlm_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79c8129bd46bfd49","mcp_get_code":{"code_sha256":"79c8129bd46bfd49"}},{"arxiv_id":"2502.04328","paper":"/paper/ola-pushing-the-frontiers-of-omni-modal","title":"Ola: Pushing the Frontiers of Omni-Modal Language Model","date":"2025-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ola-omni/ola","path":"ola/mm_utils.py","file_url":"https://github.com/ola-omni/ola/blob/HEAD/ola/mm_utils.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":"6b0f79b82970ce8b","mcp_get_code":{"code_sha256":"6b0f79b82970ce8b"}},{"arxiv_id":"2411.11066","paper":"/paper/ts-llava-constructing-visual-tokens-through","title":"TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models","date":"2024-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tingyu215/ts-llava","path":"llava/model/llava_arch.py","file_url":"https://github.com/tingyu215/ts-llava/blob/HEAD/llava/model/llava_arch.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":"35466d298191b5bd","mcp_get_code":{"code_sha256":"35466d298191b5bd"}},{"arxiv_id":"2410.11758","paper":"/paper/latent-action-pretraining-from-videos","title":"Latent Action Pretraining from Videos","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/language-table","path":"language_table/eval/wrappers.py","file_url":"https://github.com/google-research/language-table/blob/HEAD/language_table/eval/wrappers.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":"98869bb596d5d642","mcp_get_code":{"code_sha256":"98869bb596d5d642"}},{"arxiv_id":"2409.12961","paper":"/paper/oryx-mllm-on-demand-spatial-temporal","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","date":"2024-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Oryx-mllm/Oryx","path":"oryx/mm_utils.py","file_url":"https://github.com/Oryx-mllm/Oryx/blob/HEAD/oryx/mm_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"501a6aa826b9d7a5","mcp_get_code":{"code_sha256":"501a6aa826b9d7a5"}},{"arxiv_id":"1906.02283","paper":"/paper/improving-retinanet-for-ct-lesion-detection","title":"Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels","date":"2019-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fizyr/keras-retinanet","path":"keras_retinanet/backend/backend.py","file_url":"https://github.com/fizyr/keras-retinanet/blob/HEAD/keras_retinanet/backend/backend.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":"f72ab75d77478491","mcp_get_code":{"code_sha256":"f72ab75d77478491"}},{"arxiv_id":"1809.04497","paper":"/paper/hyperprior-induced-unsupervised","title":"Hyperprior Induced Unsupervised Disentanglement of Latent Representations","date":"2018-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crslab/correlated-ellipses","path":"synthetic.py","file_url":"https://github.com/crslab/correlated-ellipses/blob/HEAD/synthetic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"357d01b64500e2d2","mcp_get_code":{"code_sha256":"357d01b64500e2d2"}},{"arxiv_id":"1703.04977","paper":"/paper/what-uncertainties-do-we-need-in-bayesian","title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pmorerio/dl-uncertainty","path":"prepro.py","file_url":"https://github.com/pmorerio/dl-uncertainty/blob/HEAD/prepro.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dfd995b09189de44","mcp_get_code":{"code_sha256":"dfd995b09189de44"}},{"arxiv_id":"1611.02200","paper":"/paper/unsupervised-cross-domain-image-generation","title":"Unsupervised Cross-Domain Image Generation","date":"2016-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunjey/domain-transfer-network","path":"prepro.py","file_url":"https://github.com/yunjey/domain-transfer-network/blob/HEAD/prepro.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c870b875bc1404a","mcp_get_code":{"code_sha256":"4c870b875bc1404a"}}]}