{"url":"/sota/zero-shot-video-retrieval-on-msr-vtt","task":{"name":"Zero-Shot Video Retrieval","url":"/task/zero-shot-video-retrieval","note":null},"dataset":{"name":"MSR-VTT","url":"/dataset/msr-vtt"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Zero-shot video retrieval is the task of retrieving relevant videos based on a query (usually in text form) without any prior training on specific examples of those videos. Unlike traditional retrieval methods that rely on supervised learning with annotated datasets, zero-shot retrieval leverages pre-trained models, typically based on large-scale vision-language learning, to understand semantic relationships between textual descriptions and video content.\r\n\r\nThis approach enables retrieval of unseen video concepts by generalizing knowledge from diverse training data, making it highly useful for domains with limited labeled data, such as broadcast media, surveillance, and historical archives.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["text-to-video R@1","text-to-video R@5","text-to-video R@10","text-to-video Median Rank","text-to-video Mean Rank","video-to-text R@1","video-to-text R@5","video-to-text R@10","video-to-text Median Rank"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"text-to-video R@1":null,"text-to-video R@5":null,"text-to-video R@10":null,"text-to-video Median Rank":null,"text-to-video Mean Rank":null,"video-to-text R@1":null,"video-to-text R@5":null,"video-to-text R@10":null,"video-to-text Median Rank":null}},"counts":{"rows":41,"rows_with_code":35,"rows_with_paper_page":41,"rows_dated":41,"rows_using_additional_data":16},"rows":[{"rank_in_archive_order":1,"model":"InternVideo2-6B","metrics":{"text-to-video R@1":"55.9","text-to-video R@10":"85.1","text-to-video R@5":"78.3","video-to-text R@1":"53.7","video-to-text R@10":"84.1","video-to-text R@5":"77.5"},"uses_additional_data":true,"paper_date":"2024-03-22","paper":"/paper/internvideo2-scaling-video-foundation-models","paper_url":"https://arxiv.org/abs/2403.15377v4","paper_title":"InternVideo2: Scaling Foundation Models for Multimodal Video Understanding","code":"https://github.com/opengvlab/internvideo","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"GRAM","metrics":{"text-to-video R@1":"54.8","text-to-video R@10":"83.9","video-to-text R@1":"52.9","video-to-text R@10":"82.9"},"uses_additional_data":true,"paper_date":"2024-12-16","paper":"/paper/gramian-multimodal-representation-learning","paper_url":"https://arxiv.org/abs/2412.11959v1","paper_title":"Gramian Multimodal Representation Learning and Alignment","code":"https://github.com/ispamm/GRAM","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":10,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"InternVideo2-1B","metrics":{"text-to-video R@1":"51.9","text-to-video R@10":"82.5","text-to-video R@5":"75.3","video-to-text R@1":"50.9","video-to-text R@10":"81.8","video-to-text R@5":"73.4"},"uses_additional_data":true,"paper_date":"2024-03-22","paper":"/paper/internvideo2-scaling-video-foundation-models","paper_url":"https://arxiv.org/abs/2403.15377v4","paper_title":"InternVideo2: Scaling Foundation Models for Multimodal Video Understanding","code":"https://github.com/opengvlab/internvideo","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"VAST, HowToCaption-finetuned","metrics":{"text-to-video Median Rank":"1","text-to-video R@1":"50","text-to-video R@10":"81.4","text-to-video R@5":"73.2"},"uses_additional_data":false,"paper_date":"2023-10-07","paper":"/paper/howtocaption-prompting-llms-to-transform","paper_url":"https://arxiv.org/abs/2310.04900v2","paper_title":"HowToCaption: Prompting LLMs to Transform Video Annotations at Scale","code":"https://github.com/ninatu/howtocaption","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":5,"model":"FluxViT-B","metrics":{"text-to-video R@1":"49.9","text-to-video R@10":"79.6","text-to-video R@5":"71.0","video-to-text R@1":"49.4","video-to-text R@10":"82.4","video-to-text R@5":"73.9"},"uses_additional_data":true,"paper_date":"2025-03-18","paper":"/paper/make-your-training-flexible-towards","paper_url":"https://arxiv.org/abs/2503.14237v1","paper_title":"Make Your Training Flexible: Towards Deployment-Efficient Video Models","code":"https://github.com/opengvlab/fluxvit","n_code_links":1,"syntology":{"n_ran":14,"n_unverified":3,"n_samples":17,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"VAST","metrics":{"text-to-video R@1":"49.3","text-to-video R@10":"73.9","text-to-video R@5":"68.3"},"uses_additional_data":true,"paper_date":"2023-05-29","paper":"/paper/vast-a-vision-audio-subtitle-text-omni-1","paper_url":"https://arxiv.org/abs/2305.18500v2","paper_title":"VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset","code":"https://github.com/TXH-mercury/VALOR","n_code_links":2,"syntology":{"n_ran":15,"n_unverified":27,"n_samples":42,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"mPLUG-2","metrics":{"text-to-video R@1":"47.1","text-to-video R@10":"79.0","text-to-video R@5":"69.7"},"uses_additional_data":false,"paper_date":"2023-02-01","paper":"/paper/mplug-2-a-modularized-multi-modal-foundation","paper_url":"https://arxiv.org/abs/2302.00402v1","paper_title":"mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video","code":"https://github.com/modelscope/modelscope","n_code_links":4,"syntology":{"n_ran":14,"n_unverified":5,"n_samples":19,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"FluxViT-S","metrics":{"text-to-video R@1":"45.0","text-to-video R@10":"75.8","text-to-video R@5":"67.5","video-to-text R@1":"44.9","video-to-text R@10":"76.5","video-to-text R@5":"68.2"},"uses_additional_data":true,"paper_date":"2025-03-18","paper":"/paper/make-your-training-flexible-towards","paper_url":"https://arxiv.org/abs/2503.14237v1","paper_title":"Make Your Training Flexible: Towards Deployment-Efficient Video Models","code":"https://github.com/opengvlab/fluxvit","n_code_links":1,"syntology":{"n_ran":14,"n_unverified":3,"n_samples":17,"n_pointer_only_licence":0}},{"rank_in_archive_order":9,"model":"LanguageBind(ViT-H/14)","metrics":{"text-to-video Median Rank":"2","text-to-video R@1":"44.8","text-to-video R@10":"78.7","text-to-video R@5":"70.0","video-to-text Median Rank":"2.","video-to-text R@1":"40.9","video-to-text R@10":"75.7","video-to-text R@5":"66.4"},"uses_additional_data":true,"paper_date":"2023-10-03","paper":"/paper/languagebind-extending-video-language","paper_url":"https://arxiv.org/abs/2310.01852v7","paper_title":"LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment","code":"https://github.com/PKU-YuanGroup/Video-LLaVA","n_code_links":6,"syntology":{"n_ran":9,"n_unverified":5,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"LanguageBind(ViT-L/14)","metrics":{"text-to-video Median Rank":"2.0","text-to-video R@1":"42.8","text-to-video R@10":"76.0","text-to-video R@5":"67.5","video-to-text Median Rank":"3.0","video-to-text R@1":"38.3","video-to-text R@10":"77.8","video-to-text R@5":"65.8"},"uses_additional_data":true,"paper_date":"2023-10-03","paper":"/paper/languagebind-extending-video-language","paper_url":"https://arxiv.org/abs/2310.01852v7","paper_title":"LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment","code":"https://github.com/PKU-YuanGroup/Video-LLaVA","n_code_links":6,"syntology":{"n_ran":9,"n_unverified":5,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":11,"model":"UMT-L (ViT-L/16)","metrics":{"text-to-video R@1":"42.6","text-to-video R@10":"73.1","text-to-video R@5":"64.4","video-to-text R@1":"38.6","video-to-text R@10":"69.6","video-to-text R@5":"59.8"},"uses_additional_data":true,"paper_date":"2023-03-28","paper":"/paper/unmasked-teacher-towards-training-efficient","paper_url":"https://arxiv.org/abs/2303.16058v2","paper_title":"Unmasked Teacher: Towards Training-Efficient Video Foundation Models","code":"https://github.com/opengvlab/unmasked_teacher","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":3,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"vid-TLDR (UMT-L)","metrics":{"text-to-video R@1":"42.1","text-to-video R@10":"72.4","text-to-video R@5":"63.9","video-to-text R@1":"37.7","video-to-text R@10":"69.4","video-to-text R@5":"59.8"},"uses_additional_data":true,"paper_date":"2024-03-20","paper":"/paper/vid-tldr-training-free-token-merging-for","paper_url":"https://arxiv.org/abs/2403.13347v2","paper_title":"vid-TLDR: Training Free Token merging for Light-weight Video Transformer","code":"https://github.com/mlvlab/vid-tldr","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"BT-Adapter","metrics":{"text-to-video R@1":"40.9","text-to-video R@10":"73.5","text-to-video R@5":"64.7"},"uses_additional_data":false,"paper_date":"2023-09-27","paper":"/paper/one-for-all-video-conversation-is-feasible","paper_url":"https://arxiv.org/abs/2309.15785v2","paper_title":"BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning","code":"https://github.com/farewellthree/BT-Adapter","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"InternVideo","metrics":{"text-to-video R@1":"40.7","video-to-text R@1":"39.6"},"uses_additional_data":true,"paper_date":"2022-12-06","paper":"/paper/internvideo-general-video-foundation-models","paper_url":"https://arxiv.org/abs/2212.03191v2","paper_title":"InternVideo: General Video Foundation Models via Generative and Discriminative Learning","code":"https://github.com/opengvlab/internvideo","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":15,"model":"Florence","metrics":{"text-to-video R@1":"37.6","text-to-video R@10":"72.6","text-to-video R@5":"63.8"},"uses_additional_data":false,"paper_date":"2021-11-22","paper":"/paper/florence-a-new-foundation-model-for-computer","paper_url":"https://arxiv.org/abs/2111.11432v1","paper_title":"Florence: A New Foundation Model for Computer Vision","code":"https://github.com/microsoft/unicl","n_code_links":2,"syntology":null},{"rank_in_archive_order":16,"model":"HowToCaption","metrics":{"text-to-video Median Rank":"3","text-to-video R@1":"37.6","text-to-video R@10":"73.3","text-to-video R@5":"62"},"uses_additional_data":false,"paper_date":"2023-10-07","paper":"/paper/howtocaption-prompting-llms-to-transform","paper_url":"https://arxiv.org/abs/2310.04900v2","paper_title":"HowToCaption: Prompting LLMs to Transform Video Annotations at Scale","code":"https://github.com/ninatu/howtocaption","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":17,"model":"ImageBind","metrics":{"text-to-video R@1":"36.8","text-to-video R@10":"70.0","text-to-video R@5":"61.8"},"uses_additional_data":false,"paper_date":"2023-05-09","paper":"/paper/imagebind-one-embedding-space-to-bind-them","paper_url":"https://arxiv.org/abs/2305.05665v2","paper_title":"ImageBind: One Embedding Space To Bind Them All","code":"https://github.com/facebookresearch/imagebind","n_code_links":3,"syntology":{"n_ran":24,"n_unverified":10,"n_samples":34,"n_pointer_only_licence":32}},{"rank_in_archive_order":18,"model":"OmniVL","metrics":{"text-to-video R@1":"34.6","text-to-video R@10":"66.6","text-to-video R@5":"58.4"},"uses_additional_data":true,"paper_date":"2022-09-15","paper":"/paper/omnivl-one-foundation-model-for-image","paper_url":"https://arxiv.org/abs/2209.07526v2","paper_title":"OmniVL:One Foundation Model for Image-Language and Video-Language Tasks","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"HiTeA-17M","metrics":{"text-to-video R@1":"34.4","text-to-video R@10":"69.9","text-to-video R@5":"60.0"},"uses_additional_data":false,"paper_date":"2022-12-30","paper":"/paper/hitea-hierarchical-temporal-aware-video","paper_url":"https://arxiv.org/abs/2212.14546v1","paper_title":"HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":20,"model":"Singularity-17M","metrics":{"text-to-video R@1":"34.0","text-to-video R@10":"66.7","text-to-video R@5":"56.7"},"uses_additional_data":true,"paper_date":"2022-06-07","paper":"/paper/revealing-single-frame-bias-for-video-and","paper_url":"https://arxiv.org/abs/2206.03428v1","paper_title":"Revealing Single Frame Bias for Video-and-Language Learning","code":"https://github.com/jayleicn/ClipBERT","n_code_links":2,"syntology":{"n_ran":9,"n_unverified":3,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":21,"model":"CLIP4Clip","metrics":{"text-to-video Mean Rank":"34.0","text-to-video Median Rank":"4","text-to-video R@1":"32.0","text-to-video R@10":"66.9","text-to-video R@5":"57.0"},"uses_additional_data":false,"paper_date":"2021-04-18","paper":"/paper/clip4clip-an-empirical-study-of-clip-for-end","paper_url":"https://arxiv.org/abs/2104.08860v2","paper_title":"CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval","code":"https://github.com/towhee-io/towhee","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":3}},{"rank_in_archive_order":22,"model":"Yatai Ji et. al.","metrics":{"text-to-video R@1":"30.9","text-to-video R@10":"65.0","text-to-video R@5":"54.4"},"uses_additional_data":false,"paper_date":"2022-11-24","paper":"/paper/seeing-what-you-miss-vision-language-pre","paper_url":"https://arxiv.org/abs/2211.13437v2","paper_title":"Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning","code":"https://github.com/iigroup/scl","n_code_links":1,"syntology":null},{"rank_in_archive_order":23,"model":"HiTeA-5M","metrics":{"text-to-video R@1":"29.9","text-to-video R@10":"62.9","text-to-video R@5":"54.2"},"uses_additional_data":false,"paper_date":"2022-12-30","paper":"/paper/hitea-hierarchical-temporal-aware-video","paper_url":"https://arxiv.org/abs/2212.14546v1","paper_title":"HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":24,"model":"Singularity-5M","metrics":{"text-to-video R@1":"28.4","text-to-video R@10":"59.5","text-to-video R@5":"50.2"},"uses_additional_data":true,"paper_date":"2022-06-07","paper":"/paper/revealing-single-frame-bias-for-video-and","paper_url":"https://arxiv.org/abs/2206.03428v1","paper_title":"Revealing Single Frame Bias for Video-and-Language Learning","code":"https://github.com/jayleicn/ClipBERT","n_code_links":2,"syntology":{"n_ran":9,"n_unverified":3,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":25,"model":"Clover","metrics":{"text-to-video Median Rank":"6","text-to-video R@1":"26.4","text-to-video R@10":"60","text-to-video R@5":"49.5"},"uses_additional_data":false,"paper_date":"2022-07-16","paper":"/paper/clover-towards-a-unified-video-language","paper_url":"https://arxiv.org/abs/2207.07885v3","paper_title":"Clover: Towards A Unified Video-Language Alignment and Fusion Model","code":"https://github.com/leeyn-43/clover","n_code_links":1,"syntology":null},{"rank_in_archive_order":26,"model":"MILES","metrics":{"text-to-video Median Rank":"7","text-to-video R@1":"26.1","text-to-video R@10":"56.9","text-to-video R@5":"47.2"},"uses_additional_data":false,"paper_date":"2022-04-26","paper":"/paper/miles-visual-bert-pre-training-with-injected","paper_url":"https://arxiv.org/abs/2204.12408v1","paper_title":"MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval","code":"https://github.com/tencentarc/mcq","n_code_links":1,"syntology":null},{"rank_in_archive_order":27,"model":"Y. Ge et. al.","metrics":{"text-to-video Median Rank":"7.0","text-to-video R@1":"26.0","text-to-video R@10":"56.4","text-to-video R@5":"46.4"},"uses_additional_data":false,"paper_date":"2022-01-13","paper":"/paper/bridgeformer-bridging-video-text-retrieval","paper_url":"https://arxiv.org/abs/2201.04850v2","paper_title":"Bridging Video-text Retrieval with Multiple Choice Questions","code":"https://github.com/towhee-io/towhee","n_code_links":2,"syntology":{"n_ran":13,"n_unverified":11,"n_samples":24,"n_pointer_only_licence":6}},{"rank_in_archive_order":28,"model":"VIOLET","metrics":{"text-to-video R@1":"25.9","text-to-video R@10":"59.7","text-to-video R@5":"49.5"},"uses_additional_data":false,"paper_date":"2021-11-24","paper":"/paper/violet-end-to-end-video-language-transformers","paper_url":"https://arxiv.org/abs/2111.12681v2","paper_title":"VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling","code":"https://github.com/tsujuifu/pytorch_violet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":29,"model":"FROZEN","metrics":{"text-to-video Median Rank":"7.0","text-to-video R@1":"24.7","text-to-video R@10":"57.2","text-to-video R@5":"46.9"},"uses_additional_data":false,"paper_date":"2021-04-01","paper":"/paper/frozen-in-time-a-joint-video-and-image","paper_url":"https://arxiv.org/abs/2104.00650v2","paper_title":"Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval","code":"https://github.com/towhee-io/towhee","n_code_links":5,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":30,"model":"ALPRO","metrics":{"text-to-video Median Rank":"8","text-to-video R@1":"24.1","text-to-video R@10":"55.4","text-to-video R@5":"44.7"},"uses_additional_data":false,"paper_date":"2021-12-17","paper":"/paper/align-and-prompt-video-and-language-pre","paper_url":"https://arxiv.org/abs/2112.09583v2","paper_title":"Align and Prompt: Video-and-Language Pre-training with Entity Prompts","code":"https://github.com/salesforce/alpro","n_code_links":1,"syntology":null},{"rank_in_archive_order":31,"model":"OA-Trans","metrics":{"text-to-video Median Rank":"8.0","text-to-video R@1":"23.4","text-to-video R@10":"55.6","text-to-video R@5":"47.5"},"uses_additional_data":false,"paper_date":"2021-12-01","paper":"/paper/object-aware-video-language-pre-training-for","paper_url":"https://arxiv.org/abs/2112.00656v6","paper_title":"Object-aware Video-language Pre-training for Retrieval","code":"https://github.com/FingerRec/OA-Transformer","n_code_links":1,"syntology":null},{"rank_in_archive_order":32,"model":"LaT","metrics":{"text-to-video Median Rank":"8","text-to-video R@1":"23.4","text-to-video R@10":"53.3","text-to-video R@5":"44.1","video-to-text Median Rank":"12","video-to-text R@1":"17.2","video-to-text R@10":"47.9","video-to-text R@5":"36.2"},"uses_additional_data":false,"paper_date":"2022-07-11","paper":"/paper/lat-latent-translation-with-cycle-consistency","paper_url":"https://arxiv.org/abs/2207.04858v2","paper_title":"LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":33,"model":"A. Nagrani et. al.","metrics":{"text-to-video R@1":"19.4","text-to-video R@10":"50.3","text-to-video R@5":"39.5"},"uses_additional_data":true,"paper_date":"2022-04-01","paper":"/paper/learning-audio-video-modalities-from-image","paper_url":"https://arxiv.org/abs/2204.00679v1","paper_title":"Learning Audio-Video Modalities from Image Captions","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"HD-VILA","metrics":{"text-to-video Median Rank":"15","text-to-video R@1":"14.6","text-to-video R@10":"44.1","text-to-video R@5":"34.4"},"uses_additional_data":false,"paper_date":"2021-11-19","paper":"/paper/advancing-high-resolution-video-language","paper_url":"https://arxiv.org/abs/2111.10337v2","paper_title":"Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions","code":"https://github.com/microsoft/xpretrain","n_code_links":1,"syntology":null},{"rank_in_archive_order":35,"model":"Norton","metrics":{"text-to-video R@1":"10.7","text-to-video R@5":"24.1"},"uses_additional_data":false,"paper_date":"2024-01-30","paper":"/paper/multi-granularity-correspondence-learning-1","paper_url":"https://arxiv.org/abs/2401.16702v1","paper_title":"Multi-granularity Correspondence Learning from Long-term Noisy Videos","code":"https://github.com/XLearning-SCU/2024-ICLR-Norton","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":36,"model":"VideoCLIP","metrics":{"text-to-video R@1":"10.4","text-to-video R@10":"30.0","text-to-video R@5":"22.2"},"uses_additional_data":false,"paper_date":"2021-09-28","paper":"/paper/videoclip-contrastive-pre-training-for-zero","paper_url":"https://arxiv.org/abs/2109.14084v2","paper_title":"VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding","code":"https://github.com/facebookresearch/fairseq","n_code_links":2,"syntology":null},{"rank_in_archive_order":37,"model":"MIL-NCE","metrics":{"text-to-video Mean Rank":"29.5","text-to-video R@1":"9.9","text-to-video R@10":"32.4","text-to-video R@5":"24.0"},"uses_additional_data":false,"paper_date":"2019-12-13","paper":"/paper/end-to-end-learning-of-visual-representations","paper_url":"https://arxiv.org/abs/1912.06430v4","paper_title":"End-to-End Learning of Visual Representations from Uncurated Instructional Videos","code":"https://github.com/antoine77340/MIL-NCE_HowTo100M","n_code_links":4,"syntology":{"n_ran":3,"n_unverified":2,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":38,"model":"TACo","metrics":{"text-to-video R@1":"9.8","text-to-video R@10":"33.4","text-to-video R@5":"25.0"},"uses_additional_data":false,"paper_date":"2021-08-23","paper":"/paper/taco-token-aware-cascade-contrastive-learning","paper_url":"https://arxiv.org/abs/2108.09980v1","paper_title":"TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":39,"model":"SSML","metrics":{"text-to-video R@1":"8.0","text-to-video R@10":"29.3","text-to-video R@5":"21.3"},"uses_additional_data":false,"paper_date":"2020-03-06","paper":"/paper/noise-estimation-using-density-estimation-for","paper_url":"https://arxiv.org/abs/2003.03186v3","paper_title":"Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning","code":"https://github.com/elad-amrani/ssml","n_code_links":1,"syntology":null},{"rank_in_archive_order":40,"model":"MMT","metrics":{"text-to-video Mean Rank":"148.1","text-to-video Median Rank":"66","text-to-video R@5":"14.4"},"uses_additional_data":true,"paper_date":"2020-07-21","paper":"/paper/multi-modal-transformer-for-video-retrieval","paper_url":"https://arxiv.org/abs/2007.10639v1","paper_title":"Multi-modal Transformer for Video Retrieval","code":"https://github.com/gabeur/mmt","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":0,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":41,"model":"VATT-MBS","metrics":{"text-to-video Median Rank":"49","text-to-video R@10":"29.7"},"uses_additional_data":false,"paper_date":"2021-04-22","paper":"/paper/vatt-transformers-for-multimodal-self","paper_url":"https://arxiv.org/abs/2104.11178v3","paper_title":"VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text","code":"https://github.com/google-research/google-research/tree/master/vatt","n_code_links":5,"syntology":{"n_ran":5,"n_unverified":3,"n_samples":8,"n_pointer_only_licence":8}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 7,081 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":7081,"papers_extracted_not_yet_verified":217,"boards_without_verdict":27,"papers_not_yet_extracted":2325},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":24,"rows_with_any_sample_ran":23,"distinct_papers_with_graph_line":20,"distinct_papers_with_any_sample_ran":19,"samples_over_distinct_papers":{"n_ran":141,"n_unverified":97,"n_samples":238,"n_pointer_only_licence":56,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":177,"n_unverified":109,"n_samples":286,"n_pointer_only_licence":61,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}