{"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/vit-tiny","entry":"vit_tiny","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":40,"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":27,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":42,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":27},"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":"2602.01951","paper":"/paper/arxiv-2602-01951","title":"Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"mahmoodlab/HIPT","path":"HIPT_4K/vision_transformer.py","file_url":"https://github.com/mahmoodlab/HIPT/blob/HEAD/HIPT_4K/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0a55ce5c7c8fd9fd","mcp_get_code":{"code_sha256":"0a55ce5c7c8fd9fd"}},{"arxiv_id":"2601.21159","paper":"/paper/arxiv-2601-21159","title":"Spatial-Regularization-Aware Dual-Branch Collaborative Inference for Training-Free OVSS in Remote Sensing Imagery","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yu-ni1989/SDCI","path":"modified_clip/vision_transformer.py","file_url":"https://github.com/yu-ni1989/SDCI/blob/HEAD/modified_clip/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b8fa7e1c9f0aa21","mcp_get_code":{"code_sha256":"0b8fa7e1c9f0aa21"}},{"arxiv_id":"2512.16202","paper":"/paper/arxiv-2512-16202","title":"Open Ad-hoc Categorization with Contextualized Feature Learning","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"Wayne2Wang/OAK","path":"src/models/dino_vision_transformer.py","file_url":"https://github.com/Wayne2Wang/OAK/blob/HEAD/src/models/dino_vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2504.03755","paper":"/paper/protogcd-unified-and-unbiased-prototype","title":"ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/protogcd","path":"models/vision_transformer.py","file_url":"https://github.com/mashijie1028/protogcd/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"059df77687f34567","mcp_get_code":{"code_sha256":"059df77687f34567"}},{"arxiv_id":"2503.15141","paper":null,"title":"arXiv:2503.15141","date":null,"month_inferred_from_arxiv_id":"2025-03","title_source":null,"repo":"djukicn/ocebo","path":"models/ocebo.py","file_url":"https://github.com/djukicn/ocebo/blob/HEAD/models/ocebo.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":"7a68eb2f903dc826","mcp_get_code":{"code_sha256":"7a68eb2f903dc826"}},{"arxiv_id":"2412.16156","paper":"/paper/personalized-representation-from-personalized","title":"Personalized Representation from Personalized Generation","date":"2024-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ssundaram21/personalized-rep","path":"models/vision_transformer.py","file_url":"https://github.com/ssundaram21/personalized-rep/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"256e2853fd88f06e","mcp_get_code":{"code_sha256":"256e2853fd88f06e"}},{"arxiv_id":"2411.11409","paper":"/paper/ikea-manuals-at-work-4d-grounding-of-assembly","title":"IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunongLiu1/IKEA-Manuals-at-Work","path":"src/IKEAVideo/featurizers/DINO.py","file_url":"https://github.com/yunongLiu1/IKEA-Manuals-at-Work/blob/HEAD/src/IKEAVideo/featurizers/DINO.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9a9528c60a052e1b","mcp_get_code":{"code_sha256":"9a9528c60a052e1b"}},{"arxiv_id":"2410.19213","paper":"/paper/prototypical-hash-encoding-for-on-the-fly","title":"Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaiyangZheng/PHE","path":"vision_transformer.py","file_url":"https://github.com/HaiyangZheng/PHE/blob/HEAD/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"af7e475ba18d722d","mcp_get_code":{"code_sha256":"af7e475ba18d722d"}},{"arxiv_id":"2410.06535","paper":"/paper/happy-a-debiased-learning-framework-for","title":"Happy: A Debiased Learning Framework for Continual Generalized Category Discovery","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/Happy-CGCD","path":"models/vision_transformer.py","file_url":"https://github.com/mashijie1028/Happy-CGCD/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2410.06535","paper":"/paper/happy-a-debiased-learning-framework-for","title":"Happy: A Debiased Learning Framework for Continual Generalized Category Discovery","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/Happy-CGCD","path":"models/lora_vision_transformer.py","file_url":"https://github.com/mashijie1028/Happy-CGCD/blob/HEAD/models/lora_vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"abc2090fa89a2d35","mcp_get_code":{"code_sha256":"abc2090fa89a2d35"}},{"arxiv_id":"2410.06535","paper":"/paper/happy-a-debiased-learning-framework-for","title":"Happy: A Debiased Learning Framework for Continual Generalized Category Discovery","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/Happy-CGCD","path":"models/svft_vision_transformer.py","file_url":"https://github.com/mashijie1028/Happy-CGCD/blob/HEAD/models/svft_vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"92e76c9d03c24769","mcp_get_code":{"code_sha256":"92e76c9d03c24769"}},{"arxiv_id":"2408.14371","paper":"/paper/selex-self-expertise-in-fine-grained","title":"SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery","date":"2024-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sarahrastegar/selex","path":"models/vision_transformer.py","file_url":"https://github.com/sarahrastegar/selex/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2407.06508","paper":"/paper/a-clinical-benchmark-of-public-self","title":"A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fuchs-lab-public/opal","path":"SSL_benchmarks/code/feature_extraction/vision_transformer.py","file_url":"https://github.com/fuchs-lab-public/opal/blob/HEAD/SSL_benchmarks/code/feature_extraction/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0a55ce5c7c8fd9fd","mcp_get_code":{"code_sha256":"0a55ce5c7c8fd9fd"}},{"arxiv_id":"2406.14599","paper":"/paper/stylebreeder-exploring-and-democratizing","title":"Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stylebreeder/stylebreeder-code","path":"models/dino_vits.py","file_url":"https://github.com/stylebreeder/stylebreeder-code/blob/HEAD/models/dino_vits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91b77e6ca5ded870","mcp_get_code":{"code_sha256":"91b77e6ca5ded870"}},{"arxiv_id":"2405.14791","paper":"/paper/recurrent-early-exits-for-federated-learning","title":"Recurrent Early Exits for Federated Learning with Heterogeneous Clients","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"royson/reefl","path":"src/models/reefl_vit.py","file_url":"https://github.com/royson/reefl/blob/HEAD/src/models/reefl_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c856301a391d8154","mcp_get_code":{"code_sha256":"c856301a391d8154"}},{"arxiv_id":"2405.06283","paper":"/paper/novel-class-discovery-for-ultra-fine-grained","title":"Novel Class Discovery for Ultra-Fine-Grained Visual Categorization","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SSDUT-Caiyq/UFG-NCD","path":"models/vision_transformer.py","file_url":"https://github.com/SSDUT-Caiyq/UFG-NCD/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"92e76c9d03c24769","mcp_get_code":{"code_sha256":"92e76c9d03c24769"}},{"arxiv_id":"2403.07700","paper":"/paper/cuvler-enhanced-unsupervised-object","title":"CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shahaf-arica/CuVLER","path":"dino/vision_transformer.py","file_url":"https://github.com/shahaf-arica/CuVLER/blob/HEAD/dino/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8aede1cb23ed09bd","mcp_get_code":{"code_sha256":"8aede1cb23ed09bd"}},{"arxiv_id":"2403.06659","paper":"/paper/zero-shot-ecg-classification-with-multimodal","title":"Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cheliu-computation/merl","path":"finetune/models/vit1d.py","file_url":"https://github.com/cheliu-computation/merl/blob/HEAD/finetune/models/vit1d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bc84c9b81db6507","mcp_get_code":{"code_sha256":"5bc84c9b81db6507"}},{"arxiv_id":"2311.17893","paper":"/paper/betrayed-by-attention-a-simple-yet-effective","title":"Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shvdiwnkozbw/ssl-uvos","path":"src/model/vision_transformer.py","file_url":"https://github.com/shvdiwnkozbw/ssl-uvos/blob/HEAD/src/model/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"faaced6c07e0019e","mcp_get_code":{"code_sha256":"faaced6c07e0019e"}},{"arxiv_id":"2310.19776","paper":"/paper/learn-to-categorize-or-categorize-to-learn-1","title":"Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SarahRastegar/InfoSieve","path":"models/vision_transformer.py","file_url":"https://github.com/SarahRastegar/InfoSieve/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2308.12112","paper":"/paper/generalized-continual-category-discovery","title":"Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grypesc/camp","path":"approaches/networks/vit.py","file_url":"https://github.com/grypesc/camp/blob/HEAD/approaches/networks/vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"71f4c23ac114a32d","mcp_get_code":{"code_sha256":"71f4c23ac114a32d"}},{"arxiv_id":"2308.11796","paper":"/paper/time-does-tell-self-supervised-time-tuning-of","title":"Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smsd75/timetuning","path":"dino_vision_transformer.py","file_url":"https://github.com/smsd75/timetuning/blob/HEAD/dino_vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0a55ce5c7c8fd9fd","mcp_get_code":{"code_sha256":"0a55ce5c7c8fd9fd"}},{"arxiv_id":"2308.11063","paper":"/paper/metagcd-learning-to-continually-learn-in","title":"MetaGCD: Learning to Continually Learn in Generalized Category Discovery","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ynanwu/metagcd","path":"models/vision_transformer.py","file_url":"https://github.com/ynanwu/metagcd/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2308.01948","paper":"/paper/a-multidimensional-analysis-of-social-biases","title":"A Multidimensional Analysis of Social Biases in Vision Transformers","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jannik-brinkmann/social-biases-in-vision-transformers","path":"src/embedding_extractors/models/dino/vision_transformer.py","file_url":"https://github.com/jannik-brinkmann/social-biases-in-vision-transformers/blob/HEAD/src/embedding_extractors/models/dino/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"879e349ae38851dc","mcp_get_code":{"code_sha256":"879e349ae38851dc"}},{"arxiv_id":"2305.11435","paper":"/paper/syllable-discovery-and-cross-lingual","title":"Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jasonppy/syllable-discovery","path":"models/vision_transformer.py","file_url":"https://github.com/jasonppy/syllable-discovery/blob/HEAD/models/vision_transformer.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":"7d962f3bf8a0e816","mcp_get_code":{"code_sha256":"7d962f3bf8a0e816"}},{"arxiv_id":"2305.06144","paper":"/paper/learning-semi-supervised-gaussian-mixture","title":"Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery","date":"2023-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DTennant/GPC","path":"models/vision_transformer.py","file_url":"https://github.com/DTennant/GPC/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2303.17176","paper":"/paper/a-view-from-somewhere-human-centric-face","title":"A View From Somewhere: Human-Centric Face Representations","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yukimasano/PASS","path":"vision_transformer.py","file_url":"https://github.com/yukimasano/PASS/blob/HEAD/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0a55ce5c7c8fd9fd","mcp_get_code":{"code_sha256":"0a55ce5c7c8fd9fd"}},{"arxiv_id":"2303.15111","paper":"/paper/learning-attention-as-disentangler-for","title":"Learning Attention as Disentangler for Compositional Zero-shot Learning","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoosz/ade-czsl","path":"models/vision_transformer.py","file_url":"https://github.com/haoosz/ade-czsl/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7bba5d7d89fc9dc","mcp_get_code":{"code_sha256":"f7bba5d7d89fc9dc"}},{"arxiv_id":"2301.12246","paper":"/paper/a-closer-look-at-few-shot-classification","title":"A Closer Look at Few-shot Classification Again","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frankluox/closerlookagainfewshot","path":"architectures/backbone/DINO_ViT.py","file_url":"https://github.com/frankluox/closerlookagainfewshot/blob/HEAD/architectures/backbone/DINO_ViT.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0a55ce5c7c8fd9fd","mcp_get_code":{"code_sha256":"0a55ce5c7c8fd9fd"}},{"arxiv_id":"2212.05590","paper":"/paper/promptcal-contrastive-affinity-learning-via","title":"PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery","date":"2022-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sheng-eatamath/promptcal","path":"models/vision_transformer.py","file_url":"https://github.com/sheng-eatamath/promptcal/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f332639fc00ac198","mcp_get_code":{"code_sha256":"f332639fc00ac198"}},{"arxiv_id":"2209.03917","paper":"/paper/exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxingbin/dbot","path":"analysis/vision_transformer.py","file_url":"https://github.com/liuxingbin/dbot/blob/HEAD/analysis/vision_transformer.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":"6769eebdd8702e50","mcp_get_code":{"code_sha256":"6769eebdd8702e50"}},{"arxiv_id":"2204.13101","paper":"/paper/self-supervised-learning-of-object-parts-for","title":"Self-Supervised Learning of Object Parts for Semantic Segmentation","date":"2022-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MkuuWaUjinga/leopart","path":"src/vit.py","file_url":"https://github.com/MkuuWaUjinga/leopart/blob/HEAD/src/vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1e452e1b1176484","mcp_get_code":{"code_sha256":"c1e452e1b1176484"}},{"arxiv_id":"2204.07305","paper":"/paper/pushing-the-limits-of-simple-pipelines-for","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","date":"2022-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hushell/pmf_cvpr22","path":"models/vision_transformer.py","file_url":"https://github.com/hushell/pmf_cvpr22/blob/HEAD/models/vision_transformer.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":"bd29518a98e56cf5","mcp_get_code":{"code_sha256":"bd29518a98e56cf5"}},{"arxiv_id":"2203.14415","paper":"/paper/mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/mugs","path":"src/vision_transformer.py","file_url":"https://github.com/sail-sg/mugs/blob/HEAD/src/vision_transformer.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":"f5479cb822d15445","mcp_get_code":{"code_sha256":"f5479cb822d15445"}},{"arxiv_id":"2203.08414","paper":"/paper/unsupervised-semantic-segmentation-by-2","title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leggedrobotics/self_supervised_segmentation","path":"stego/backbones/dino/vision_transformer.py","file_url":"https://github.com/leggedrobotics/self_supervised_segmentation/blob/HEAD/stego/backbones/dino/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a250f0f34719cf17","mcp_get_code":{"code_sha256":"a250f0f34719cf17"}},{"arxiv_id":"2202.11929","paper":"/paper/word-segmentation-on-discovered-phone-units","title":"Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring","date":"2022-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jasonppy/word-discovery","path":"models/vision_transformer.py","file_url":"https://github.com/jasonppy/word-discovery/blob/HEAD/models/vision_transformer.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":"7d962f3bf8a0e816","mcp_get_code":{"code_sha256":"7d962f3bf8a0e816"}},{"arxiv_id":"2201.02609","paper":"/paper/generalized-category-discovery","title":"Generalized Category Discovery","date":"2022-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sgvaze/generalized-category-discovery","path":"models/vision_transformer.py","file_url":"https://github.com/sgvaze/generalized-category-discovery/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"2111.12958","paper":"/paper/self-distilled-self-supervised-representation","title":"Self-Distilled Self-Supervised Representation Learning","date":"2021-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hagiss/sdssl","path":"vision_transformer.py","file_url":"https://github.com/hagiss/sdssl/blob/HEAD/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"467133f2b3c000e2","mcp_get_code":{"code_sha256":"467133f2b3c000e2"}},{"arxiv_id":"2007.09384","paper":"/paper/multi-scale-positive-sample-refinement-for","title":"Multi-Scale Positive Sample Refinement for Few-Shot Object Detection","date":"2020-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bohao-lee/pdc","path":"models/vision_transformer.py","file_url":"https://github.com/bohao-lee/pdc/blob/HEAD/models/vision_transformer.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":"1b5e2f11314f237d","mcp_get_code":{"code_sha256":"1b5e2f11314f237d"}},{"arxiv_id":"openreview_YA3AvVx2Ze","paper":null,"title":"arXiv:openreview_YA3AvVx2Ze","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lytang63/CoGe-GCD","path":"models/vision_transformer.py","file_url":"https://github.com/lytang63/CoGe-GCD/blob/HEAD/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}},{"arxiv_id":"aaai_28502","paper":null,"title":"arXiv:aaai_28502","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yuhongtian17/Spatial-Transform-Decoupling","path":"mmrotate-main/models/vision_transformer.py","file_url":"https://github.com/yuhongtian17/Spatial-Transform-Decoupling/blob/HEAD/mmrotate-main/models/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b5e2f11314f237d","mcp_get_code":{"code_sha256":"1b5e2f11314f237d"}},{"arxiv_id":"Du_On-the-Fly_Category_Discovery_CVPR_2023_paper","paper":null,"title":"arXiv:Du_On-the-Fly_Category_Discovery_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PRIS-CV/On-the-fly-Category-Discovery","path":"vision_transformer.py","file_url":"https://github.com/PRIS-CV/On-the-fly-Category-Discovery/blob/HEAD/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"996780ba4be89ba3","mcp_get_code":{"code_sha256":"996780ba4be89ba3"}}]}