{"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/build-model-from-openai-state-dict","entry":"build_model_from_openai_state_dict","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":42,"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":30,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":45,"n_places_pointer_only":20,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":30},"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":"2609.12454","paper":"/paper/arxiv-2609-12454","title":"Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"JuzhengMiao/Spatial-FAD","path":"CLIP/model.py","file_url":"https://github.com/JuzhengMiao/Spatial-FAD/blob/HEAD/CLIP/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09c0abc70500a134","mcp_get_code":{"code_sha256":"09c0abc70500a134"}},{"arxiv_id":"2605.30107","paper":"/paper/arxiv-2605-30107","title":"Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"LAION-AI/CLAP","path":"src/laion_clap/clap_module/model.py","file_url":"https://github.com/LAION-AI/CLAP/blob/HEAD/src/laion_clap/clap_module/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"50d4bb09e01d4068","mcp_get_code":{"code_sha256":"50d4bb09e01d4068"}},{"arxiv_id":"2505.15816","paper":"/paper/streamline-without-sacrifice-squeeze-out","title":"Streamline Without Sacrifice -- Squeeze out Computation Redundancy in LMM","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"penghao-wu/proxyv","path":"llava/model/multimodal_encoder/dev_eva_clip/eva_clip/model.py","file_url":"https://github.com/penghao-wu/proxyv/blob/HEAD/llava/model/multimodal_encoder/dev_eva_clip/eva_clip/model.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":"a8e9981d9caacade","mcp_get_code":{"code_sha256":"a8e9981d9caacade"}},{"arxiv_id":"2503.19900","paper":"/paper/cafe-unifying-representation-and-generation","title":"CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuning","date":"2025-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"275eb8fe6104bf3a","mcp_get_code":{"code_sha256":"275eb8fe6104bf3a"}},{"arxiv_id":"2501.13926","paper":"/paper/can-we-generate-images-with-cot-let-s-verify","title":"Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step","date":"2025-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"275eb8fe6104bf3a","mcp_get_code":{"code_sha256":"275eb8fe6104bf3a"}},{"arxiv_id":"2412.10372","paper":"/paper/unimed-clip-towards-a-unified-image-text","title":"UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbzuai-oryx/unimed-clip","path":"src/open_clip/model.py","file_url":"https://github.com/mbzuai-oryx/unimed-clip/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4bdb7789fe2f485e","mcp_get_code":{"code_sha256":"4bdb7789fe2f485e"}},{"arxiv_id":"2412.04653","paper":"/paper/hidden-in-the-noise-two-stage-robust","title":"Hidden in the Noise: Two-Stage Robust Watermarking for Images","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kasraarabi/Hidden-in-the-Noise","path":"open_clip/model.py","file_url":"https://github.com/Kasraarabi/Hidden-in-the-Noise/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"57f1602ed20a9c21","mcp_get_code":{"code_sha256":"57f1602ed20a9c21"}},{"arxiv_id":"2411.15024","paper":"/paper/dycoke-dynamic-compression-of-tokens-for-fast","title":"DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models","date":"2024-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"275eb8fe6104bf3a","mcp_get_code":{"code_sha256":"275eb8fe6104bf3a"}},{"arxiv_id":"2411.03862","paper":"/paper/robin-robust-and-invisible-watermarks-for","title":"ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hannah1102/ROBIN","path":"open_clip/model.py","file_url":"https://github.com/Hannah1102/ROBIN/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"57f1602ed20a9c21","mcp_get_code":{"code_sha256":"57f1602ed20a9c21"}},{"arxiv_id":"2410.05255","paper":"/paper/seppo-semi-policy-preference-optimization-for","title":"SePPO: Semi-Policy Preference Optimization for Diffusion Alignment","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dwanzhang-ai/seppo","path":"utils/open_clip/model.py","file_url":"https://github.com/dwanzhang-ai/seppo/blob/HEAD/utils/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"53fc85e1b2889c0f","mcp_get_code":{"code_sha256":"53fc85e1b2889c0f"}},{"arxiv_id":"2410.00320","paper":"/paper/pointad-comprehending-3d-anomalies-from","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/accurate-winclip-pytorch","path":"src/open_clip/model.py","file_url":"https://github.com/zqhang/accurate-winclip-pytorch/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7bca1358293c8ab","mcp_get_code":{"code_sha256":"b7bca1358293c8ab"}},{"arxiv_id":"2407.15795","paper":"/paper/adaclip-adapting-clip-with-hybrid-learnable","title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caoyunkang/adaclip","path":"method/clip_model.py","file_url":"https://github.com/caoyunkang/adaclip/blob/HEAD/method/clip_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5d5046aaa43f74c7","mcp_get_code":{"code_sha256":"5d5046aaa43f74c7"}},{"arxiv_id":"2407.06491","paper":"/paper/videoeval-comprehensive-benchmark-suite-for","title":"VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leexinhao/VideoEval","path":"VidTAB_Zeroshot/eva_clip/model.py","file_url":"https://github.com/leexinhao/VideoEval/blob/HEAD/VidTAB_Zeroshot/eva_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b34d6c17c6e09a04","mcp_get_code":{"code_sha256":"b34d6c17c6e09a04"}},{"arxiv_id":"2405.00740","paper":"/paper/modeling-caption-diversity-in-contrastive","title":"Modeling Caption Diversity in Contrastive Vision-Language Pretraining","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/llip","path":"llip/open_clip/model.py","file_url":"https://github.com/facebookresearch/llip/blob/HEAD/llip/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"546ba17b219740cb","mcp_get_code":{"code_sha256":"546ba17b219740cb"}},{"arxiv_id":"2404.16022","paper":"/paper/pulid-pure-and-lightning-id-customization-via","title":"PuLID: Pure and Lightning ID Customization via Contrastive Alignment","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ToTheBeginning/PuLID","path":"eva_clip/model.py","file_url":"https://github.com/ToTheBeginning/PuLID/blob/HEAD/eva_clip/model.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":"e3f7350246f3e4bb","mcp_get_code":{"code_sha256":"e3f7350246f3e4bb"}},{"arxiv_id":"2404.16022","paper":"/paper/pulid-pure-and-lightning-id-customization-via","title":"PuLID: Pure and Lightning ID Customization via Contrastive Alignment","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zsxkib/PuLID","path":"eva_clip/model.py","file_url":"https://github.com/zsxkib/PuLID/blob/HEAD/eva_clip/model.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":"b34d6c17c6e09a04","mcp_get_code":{"code_sha256":"b34d6c17c6e09a04"}},{"arxiv_id":"2404.14055","paper":"/paper/ringid-rethinking-tree-ring-watermarking-for","title":"RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification","date":"2024-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/ringid","path":"open_clip/model.py","file_url":"https://github.com/showlab/ringid/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"57f1602ed20a9c21","mcp_get_code":{"code_sha256":"57f1602ed20a9c21"}},{"arxiv_id":"2404.13671","paper":"/paper/filo-zero-shot-anomaly-detection-by-fine","title":"FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization","date":"2024-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"casia-iva-lab/filo","path":"models/vv_open_clip/model.py","file_url":"https://github.com/casia-iva-lab/filo/blob/HEAD/models/vv_open_clip/model.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":"566817905973a0e8","mcp_get_code":{"code_sha256":"566817905973a0e8"}},{"arxiv_id":"2404.05231","paper":"/paper/promptad-learning-prompts-with-only-normal","title":"PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"funz-0/promptad","path":"PromptAD/CLIPAD/model.py","file_url":"https://github.com/funz-0/promptad/blob/HEAD/PromptAD/CLIPAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"code_sha256_prefix":"c92f832f94b5a1c1","mcp_get_code":{"code_sha256":"c92f832f94b5a1c1"}},{"arxiv_id":"2404.04956","paper":"/paper/gaussian-shading-provable-performance","title":"Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models","date":"2024-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bsmhmmlf/Gaussian-Shading","path":"open_clip/model.py","file_url":"https://github.com/bsmhmmlf/Gaussian-Shading/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"57f1602ed20a9c21","mcp_get_code":{"code_sha256":"57f1602ed20a9c21"}},{"arxiv_id":"2403.12570","paper":"/paper/adapting-visual-language-models-for","title":"Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mediabrain-sjtu/mvfa-ad","path":"CLIP/model.py","file_url":"https://github.com/mediabrain-sjtu/mvfa-ad/blob/HEAD/CLIP/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09c0abc70500a134","mcp_get_code":{"code_sha256":"09c0abc70500a134"}},{"arxiv_id":"2312.01886","paper":"/paper/instructta-instruction-tuned-targeted-attack","title":"InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xunguangwang/instructta","path":"EVA-CLIP/rei/eva_clip/model.py","file_url":"https://github.com/xunguangwang/instructta/blob/HEAD/EVA-CLIP/rei/eva_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b34d6c17c6e09a04","mcp_get_code":{"code_sha256":"b34d6c17c6e09a04"}},{"arxiv_id":"2311.18803","paper":"/paper/bioclip-a-vision-foundation-model-for-the","title":"BioCLIP: A Vision Foundation Model for the Tree of Life","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imageomics/bioclip","path":"src/open_clip/model.py","file_url":"https://github.com/imageomics/bioclip/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"57f1602ed20a9c21","mcp_get_code":{"code_sha256":"57f1602ed20a9c21"}},{"arxiv_id":"2311.17048","paper":"/paper/zero-shot-referring-expression-comprehension","title":"Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"show-han/zeroshot_rec","path":"VLA_finetune/open_clip/model.py","file_url":"https://github.com/show-han/zeroshot_rec/blob/HEAD/VLA_finetune/open_clip/model.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":"e209d001d7c966cf","mcp_get_code":{"code_sha256":"e209d001d7c966cf"}},{"arxiv_id":"2311.12908","paper":"/paper/diffusion-model-alignment-using-direct","title":"Diffusion Model Alignment Using Direct Preference Optimization","date":"2023-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SalesforceAIResearch/DiffusionDPO","path":"utils/open_clip/model.py","file_url":"https://github.com/SalesforceAIResearch/DiffusionDPO/blob/HEAD/utils/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"53fc85e1b2889c0f","mcp_get_code":{"code_sha256":"53fc85e1b2889c0f"}},{"arxiv_id":"2310.18961","paper":"/paper/anomalyclip-object-agnostic-prompt-learning","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/WinCLIP-pytorch","path":"src/open_clip/model.py","file_url":"https://github.com/zqhang/WinCLIP-pytorch/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e332828701fffe70","mcp_get_code":{"code_sha256":"e332828701fffe70"}},{"arxiv_id":"2310.18961","paper":"/paper/anomalyclip-object-agnostic-prompt-learning","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/WinCLIP-pytorch","path":"src/open_clip/model_revise.py","file_url":"https://github.com/zqhang/WinCLIP-pytorch/blob/HEAD/src/open_clip/model_revise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e0b318791c717866","mcp_get_code":{"code_sha256":"e0b318791c717866"}},{"arxiv_id":"2310.03744","paper":"/paper/improved-baselines-with-visual-instruction","title":"Improved Baselines with Visual Instruction Tuning","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dinhvietcuong1996/icme25-inova","path":"llava/model/multimodal_encoder/dev_eva_clip/eva_clip/model.py","file_url":"https://github.com/dinhvietcuong1996/icme25-inova/blob/HEAD/llava/model/multimodal_encoder/dev_eva_clip/eva_clip/model.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":"275eb8fe6104bf3a","mcp_get_code":{"code_sha256":"275eb8fe6104bf3a"}},{"arxiv_id":"2309.17002","paper":"/paper/understanding-and-mitigating-the-label-noise","title":"Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hhhhhhao/Noisy-Model-Learning","path":"open_clip/open_clip/model.py","file_url":"https://github.com/Hhhhhhao/Noisy-Model-Learning/blob/HEAD/open_clip/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b4b0d1ac0c85692","mcp_get_code":{"code_sha256":"8b4b0d1ac0c85692"}},{"arxiv_id":"2308.15939","paper":"/paper/anovl-adapting-vision-language-models-for","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hq-deng/AnoVL","path":"open_clip/model.py","file_url":"https://github.com/hq-deng/AnoVL/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"566817905973a0e8","mcp_get_code":{"code_sha256":"566817905973a0e8"}},{"arxiv_id":"2308.12213","paper":"/paper/clipn-for-zero-shot-ood-detection-teaching","title":"CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmed-lab/clipn","path":"src/open_clip/model.py","file_url":"https://github.com/xmed-lab/clipn/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"630cd10269e63c8c","mcp_get_code":{"code_sha256":"630cd10269e63c8c"}},{"arxiv_id":"2308.05734","paper":"/paper/audioldm-2-learning-holistic-audio-generation","title":"AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoheliu/AudioLDM2","path":"audioldm2/clap/open_clip/model.py","file_url":"https://github.com/haoheliu/AudioLDM2/blob/HEAD/audioldm2/clap/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"fde7529206b8c46f","mcp_get_code":{"code_sha256":"fde7529206b8c46f"}},{"arxiv_id":"2307.12732","paper":"/paper/clip-kd-an-empirical-study-of-distilling-clip","title":"CLIP-KD: An Empirical Study of CLIP Model Distillation","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"winycg/clip-kd","path":"src/open_clip/model.py","file_url":"https://github.com/winycg/clip-kd/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ae9f5eab925c792","mcp_get_code":{"code_sha256":"1ae9f5eab925c792"}},{"arxiv_id":"2306.17203","paper":"/paper/diff-foley-synchronized-video-to-audio-1","title":"Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luosiallen/Diff-Foley","path":"training/open_cavp_main/src/open_clip/model.py","file_url":"https://github.com/luosiallen/Diff-Foley/blob/HEAD/training/open_cavp_main/src/open_clip/model.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":"e4c056f11a6a6acf","mcp_get_code":{"code_sha256":"e4c056f11a6a6acf"}},{"arxiv_id":"2305.17382","paper":"/paper/a-zero-few-shot-anomaly-classification-and","title":"APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD","date":"2023-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bychelsea/vand-april-gan","path":"open_clip/model.py","file_url":"https://github.com/bychelsea/vand-april-gan/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"096be08051b5c0d0","mcp_get_code":{"code_sha256":"096be08051b5c0d0"}},{"arxiv_id":"2304.05884","paper":"/paper/unicom-universal-and-compact-representation","title":"Unicom: Universal and Compact Representation Learning for Image Retrieval","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"275eb8fe6104bf3a","mcp_get_code":{"code_sha256":"275eb8fe6104bf3a"}},{"arxiv_id":"2303.14814","paper":"/paper/winclip-zero-few-shot-anomaly-classification","title":"WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation","date":"2023-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqhang/Accurate-WinCLIP-pytorch","path":"src/open_clip/model.py","file_url":"https://github.com/zqhang/Accurate-WinCLIP-pytorch/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e332828701fffe70","mcp_get_code":{"code_sha256":"e332828701fffe70"}},{"arxiv_id":"2303.14814","paper":"/paper/winclip-zero-few-shot-anomaly-classification","title":"WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation","date":"2023-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caoyunkang/WinClip","path":"WinCLIP/CLIPAD/model.py","file_url":"https://github.com/caoyunkang/WinClip/blob/HEAD/WinCLIP/CLIPAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c94ec43ef54519ef","mcp_get_code":{"code_sha256":"c94ec43ef54519ef"}},{"arxiv_id":"2302.10281","paper":"/paper/lit-tuned-models-for-efficient-species","title":"LiT Tuned Models for Efficient Species Detection","date":"2023-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"903b104e9c495329","mcp_get_code":{"code_sha256":"903b104e9c495329"}},{"arxiv_id":"2302.03668","paper":"/paper/hard-prompts-made-easy-gradient-based-1","title":"Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery","date":"2023-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuxinWenRick/hard-prompts-made-easy","path":"open_clip/model.py","file_url":"https://github.com/YuxinWenRick/hard-prompts-made-easy/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10a858bd32dad1e6","mcp_get_code":{"code_sha256":"10a858bd32dad1e6"}},{"arxiv_id":"2301.07094","paper":"/paper/learning-customized-visual-models-with","title":"Learning Customized Visual Models with Retrieval-Augmented Knowledge","date":"2023-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/react","path":"react_customization/src/open_clip/model.py","file_url":"https://github.com/microsoft/react/blob/HEAD/react_customization/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26a799d4c5d477a1","mcp_get_code":{"code_sha256":"26a799d4c5d477a1"}},{"arxiv_id":"2210.04150","paper":"/paper/open-vocabulary-semantic-segmentation-with","title":"Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP","date":"2022-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/ov-seg","path":"open_clip_training/src/open_clip/model.py","file_url":"https://github.com/facebookresearch/ov-seg/blob/HEAD/open_clip_training/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b9b4b4cc71fba508","mcp_get_code":{"code_sha256":"b9b4b4cc71fba508"}},{"arxiv_id":"2103.00020","paper":"/paper/learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NYU-DICE-Lab/open_clip","path":"src/open_clip/model.py","file_url":"https://github.com/NYU-DICE-Lab/open_clip/blob/HEAD/src/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"903b104e9c495329","mcp_get_code":{"code_sha256":"903b104e9c495329"}},{"arxiv_id":"Ma_ReMP-AD_Retrieval-enhanced_Multi-modal_Prompt_Fusion_for_Few-Shot_Industrial_Visual_Anomaly_ICCV_2025_paper","paper":null,"title":"arXiv:Ma_ReMP-AD_Retrieval-enhanced_Multi-modal_Prompt_Fusion_for_Few-Shot_Industrial_Visual_Anomaly_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cshcma/ReMP-AD","path":"open_clip/model.py","file_url":"https://github.com/cshcma/ReMP-AD/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b968a2166183191","mcp_get_code":{"code_sha256":"8b968a2166183191"}},{"arxiv_id":"Hertz_Style_Aligned_Image_Generation_via_Shared_Attention_CVPR_2024_paper","paper":null,"title":"arXiv:Hertz_Style_Aligned_Image_Generation_via_Shared_Attention_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"aim-uofa/StyleDrop-PyTorch","path":"open_clip/model.py","file_url":"https://github.com/aim-uofa/StyleDrop-PyTorch/blob/HEAD/open_clip/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b42328328b7afbf8","mcp_get_code":{"code_sha256":"b42328328b7afbf8"}}]}