{"url":"/sota/video-retrieval-on-lsmdc","task":{"name":"Video Retrieval","url":"/task/video-retrieval","note":null},"dataset":{"name":"LSMDC","url":"/dataset/lsmdc"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query.  Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.","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@10","video-to-text R@5","video-to-text Median Rank","video-to-text Mean 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@10":null,"video-to-text R@5":null,"video-to-text Median Rank":null,"video-to-text Mean Rank":null}},"counts":{"rows":38,"rows_with_code":33,"rows_with_paper_page":38,"rows_dated":38,"rows_using_additional_data":22},"rows":[{"rank_in_archive_order":1,"model":"InternVideo2-6B","metrics":{"text-to-video R@1":"46.4","video-to-text R@1":"46.7"},"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":"vid-TLDR (UMT-L)","metrics":{"text-to-video R@1":"43.1","text-to-video R@10":"71.4","text-to-video R@5":"64.5","video-to-text R@1":"40.7","video-to-text R@10":"63.6","video-to-text R@5":"70.2"},"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":3,"model":"UMT-L (ViT-L/16)","metrics":{"text-to-video R@1":"43.0","text-to-video R@10":"73.0","text-to-video R@5":"65.5","video-to-text R@1":"41.4","video-to-text R@10":"71.5","video-to-text R@5":"64.3"},"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":3,"n_unverified":5,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"HunYuan_tvr (huge)","metrics":{"text-to-video Mean Rank":"3.9","text-to-video Median Rank":"2.0","text-to-video R@1":"40.4","text-to-video R@10":"92.8","text-to-video R@5":"80.1","video-to-text Mean Rank":"4.3","video-to-text Median Rank":"2.0","video-to-text R@1":"34.6 ","video-to-text R@10":"91.8","video-to-text R@5":"71.8"},"uses_additional_data":true,"paper_date":"2022-04-07","paper":"/paper/hunyuan-tvr-for-text-video-retrivial","paper_url":"https://arxiv.org/abs/2204.03382v8","paper_title":"Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"COSA","metrics":{"text-to-video R@1":"39.4"},"uses_additional_data":true,"paper_date":"2023-06-15","paper":"/paper/cosa-concatenated-sample-pretrained-vision","paper_url":"https://arxiv.org/abs/2306.09085v1","paper_title":"COSA: Concatenated Sample Pretrained Vision-Language Foundation Model","code":"https://github.com/txh-mercury/cosa","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"mPLUG-2","metrics":{"text-to-video R@1":"34.4","text-to-video R@10":"65.1","text-to-video R@5":"55.2"},"uses_additional_data":true,"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":9,"n_unverified":10,"n_samples":19,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"VALOR","metrics":{"text-to-video R@1":"34.2","text-to-video R@10":"64.1","text-to-video R@5":"56.0"},"uses_additional_data":true,"paper_date":"2023-04-17","paper":"/paper/valor-vision-audio-language-omni-perception","paper_url":"https://arxiv.org/abs/2304.08345v2","paper_title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","code":"https://github.com/TXH-mercury/VALOR","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"InternVideo","metrics":{"text-to-video R@1":"34.0","video-to-text R@1":"34.9"},"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":9,"model":"CLIP-ViP","metrics":{"text-to-video Median Rank":"5","text-to-video R@1":"30.7","text-to-video R@10":"60.6","text-to-video R@5":"51.4"},"uses_additional_data":true,"paper_date":"2022-09-14","paper":"/paper/clip-vip-adapting-pre-trained-image-text","paper_url":"https://arxiv.org/abs/2209.06430v4","paper_title":"CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment","code":"https://github.com/microsoft/xpretrain","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":10,"model":"HunYuan_tvr","metrics":{"text-to-video Mean Rank":"56.4","text-to-video Median Rank":"7","text-to-video R@1":"29.7","text-to-video R@10":"55.4","text-to-video R@5":"46.4","video-to-text Mean Rank":"48.9","video-to-text Median Rank":"7","video-to-text R@1":"30.1","video-to-text R@10":"55.7","video-to-text R@5":"47.5"},"uses_additional_data":true,"paper_date":"2022-04-07","paper":"/paper/hunyuan-tvr-for-text-video-retrivial","paper_url":"https://arxiv.org/abs/2204.03382v8","paper_title":"Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"STAN","metrics":{"text-to-video Median Rank":"6","text-to-video R@1":"29.2","text-to-video R@10":"58.8","text-to-video R@5":"49.5"},"uses_additional_data":true,"paper_date":"2023-01-26","paper":"/paper/revisiting-temporal-modeling-for-clip-based","paper_url":"https://arxiv.org/abs/2301.11116v1","paper_title":"Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring","code":"https://github.com/farewellthree/stan","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"HiTeA","metrics":{"text-to-video R@1":"28.7","text-to-video R@10":"59.0","text-to-video R@5":"50.3"},"uses_additional_data":true,"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":13,"model":"MDMMT-2","metrics":{"text-to-video Mean Rank":"48.0","text-to-video Median Rank":"6.7","text-to-video R@1":"26.9","text-to-video R@10":"55.9","text-to-video R@5":"46.7"},"uses_additional_data":true,"paper_date":"2022-03-14","paper":"/paper/mdmmt-2-multidomain-multimodal-transformer","paper_url":"https://arxiv.org/abs/2203.07086v1","paper_title":"MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One More Step Towards Generalization","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":14,"model":"X-CLIP","metrics":{"text-to-video R@1":"26.1","video-to-text R@1":"26.9"},"uses_additional_data":false,"paper_date":"2022-07-15","paper":"/paper/x-clip-end-to-end-multi-grained-contrastive","paper_url":"https://arxiv.org/abs/2207.07285v2","paper_title":"X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval","code":"https://github.com/xuguohai/X-CLIP","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":1}},{"rank_in_archive_order":15,"model":"EMCL-Net++","metrics":{"text-to-video R@1":"25.9","text-to-video R@5":"46.4","video-to-text Mean Rank":"8","video-to-text R@1":"26.7","video-to-text R@10":"54.4","video-to-text R@5":"44.7"},"uses_additional_data":false,"paper_date":"2022-11-21","paper":"/paper/expectation-maximization-contrastive-learning","paper_url":"https://arxiv.org/abs/2211.11427v1","paper_title":"Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations","code":"https://github.com/jpthu17/emcl","n_code_links":4,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":16,"model":"CAMoE","metrics":{"text-to-video Mean Rank":"54.4","text-to-video R@1":"25.9","text-to-video R@10":"53.7","text-to-video R@5":"46.1"},"uses_additional_data":true,"paper_date":"2021-09-09","paper":"/paper/improving-video-text-retrieval-by-multi","paper_url":"https://arxiv.org/abs/2109.04290v3","paper_title":"Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss","code":"https://github.com/starmemda/camow","n_code_links":2,"syntology":null},{"rank_in_archive_order":17,"model":"X-Pool","metrics":{"text-to-video Mean Rank":"53.2","text-to-video Median Rank":"8.0","text-to-video R@1":"25.2","text-to-video R@10":"53.5","text-to-video R@5":"43.7","video-to-text Mean Rank":"47.4","video-to-text Median Rank":"10.0","video-to-text R@1":"22.7","video-to-text R@10":"51.2","video-to-text R@5":"42.6"},"uses_additional_data":true,"paper_date":"2022-03-28","paper":"/paper/x-pool-cross-modal-language-video-attention","paper_url":"https://arxiv.org/abs/2203.15086v1","paper_title":"X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval","code":"https://github.com/layer6ai-labs/xpool","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":18,"model":"Clover","metrics":{"text-to-video Median Rank":"8","text-to-video R@1":"24.8","text-to-video R@10":"54.5","text-to-video R@5":"44"},"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":19,"model":"DiffusionRet","metrics":{"text-to-video Mean Rank":"40.7","text-to-video Median Rank":"8.0","text-to-video R@1":"24.4","text-to-video R@10":"54.3","text-to-video R@5":"43.1","video-to-text Mean Rank":"40.2","video-to-text Median Rank":"9.0","video-to-text R@1":"23.0","video-to-text R@10":"51.5","video-to-text R@5":"43.5"},"uses_additional_data":false,"paper_date":"2023-03-17","paper":"/paper/diffusionret-generative-text-video-retrieval","paper_url":"https://arxiv.org/abs/2303.09867v2","paper_title":"DiffusionRet: Generative Text-Video Retrieval with Diffusion Model","code":"https://github.com/jpthu17/emcl","n_code_links":4,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":20,"model":"CenterCLIP (ViT-B/16)","metrics":{"text-to-video Mean Rank":"47.3","text-to-video Median Rank":"8","text-to-video R@1":"24.2","text-to-video R@10":"55.9","text-to-video R@5":"46.2","video-to-text Mean Rank":"41.3","video-to-text Median Rank":"7","video-to-text R@1":"24.5","video-to-text R@10":"55.8","video-to-text R@5":"46.4"},"uses_additional_data":true,"paper_date":"2022-05-02","paper":"/paper/centerclip-token-clustering-for-efficient","paper_url":"https://arxiv.org/abs/2205.00823v1","paper_title":"CenterCLIP: Token Clustering for Efficient Text-Video Retrieval","code":"https://github.com/mzhaoshuai/CenterCLIP","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":21,"model":"VIOLETv2","metrics":{"text-to-video R@1":"24","text-to-video R@10":"54.1","text-to-video R@5":"43.5"},"uses_additional_data":false,"paper_date":"2022-09-04","paper":"/paper/an-empirical-study-of-end-to-end-video","paper_url":"https://arxiv.org/abs/2209.01540v5","paper_title":"An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling","code":"https://github.com/tsujuifu/pytorch_empirical-mvm","n_code_links":1,"syntology":null},{"rank_in_archive_order":22,"model":"EMCL-Net","metrics":{"text-to-video R@1":"23.9","text-to-video R@10":"50.9","text-to-video R@5":"42.4","video-to-text Mean Rank":"12","video-to-text R@1":"22.2","video-to-text R@10":"49.2","video-to-text R@5":"40.6"},"uses_additional_data":false,"paper_date":"2022-11-21","paper":"/paper/expectation-maximization-contrastive-learning","paper_url":"https://arxiv.org/abs/2211.11427v1","paper_title":"Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations","code":"https://github.com/jpthu17/emcl","n_code_links":4,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":23,"model":"QB-Norm+CLIP4Clip","metrics":{"text-to-video Median Rank":"11.0","text-to-video R@1":"22.4","text-to-video R@10":"49.5","text-to-video R@5":"40.1"},"uses_additional_data":true,"paper_date":"2021-12-23","paper":"/paper/cross-modal-retrieval-with-querybank","paper_url":"https://arxiv.org/abs/2112.12777v3","paper_title":"Cross Modal Retrieval with Querybank Normalisation","code":"https://github.com/ioanacroi/qb-norm","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":24,"model":"CLIP4Clip","metrics":{"text-to-video Mean Rank":"58.0","text-to-video R@1":"21.6","text-to-video R@10":"49.8","text-to-video R@5":"41.8"},"uses_additional_data":true,"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":25,"model":"MDMMT","metrics":{"text-to-video Mean Rank":"58.0","text-to-video Median Rank":"12.3","text-to-video R@1":"18.8","text-to-video R@10":"47.9","text-to-video R@5":"38.5"},"uses_additional_data":true,"paper_date":"2021-03-19","paper":"/paper/mdmmt-multidomain-multimodal-transformer-for","paper_url":"https://arxiv.org/abs/2103.10699v1","paper_title":"MDMMT: Multidomain Multimodal Transformer for Video Retrieval","code":"https://github.com/towhee-io/towhee","n_code_links":3,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":26,"model":"HD-VILA","metrics":{"text-to-video Median Rank":"15","text-to-video R@1":"17.4","text-to-video R@10":"44.1","text-to-video R@5":"34.1"},"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":27,"model":"FROZEN","metrics":{"text-to-video Median Rank":"20.0","text-to-video R@1":"15.0","text-to-video R@10":"39.8","text-to-video R@5":"30.8"},"uses_additional_data":true,"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":3,"n_unverified":8,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":28,"model":"Ours","metrics":{"text-to-video R@1":"14.9","text-to-video R@5":"33.2","video-to-text R@1":"15.3","video-to-text R@5":"34.1"},"uses_additional_data":false,"paper_date":"2021-10-21","paper":"/paper/video-and-text-matching-with-conditioned","paper_url":"https://arxiv.org/abs/2110.11298v1","paper_title":"Video and Text Matching with Conditioned Embeddings","code":"https://github.com/ameenali/videomatch","n_code_links":1,"syntology":null},{"rank_in_archive_order":29,"model":"MMT-Pretrained","metrics":{"text-to-video Median Rank":"19.3","text-to-video R@1":"13.5","text-to-video R@10":"40.1","text-to-video R@5":"29.9"},"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":1,"n_unverified":6,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":30,"model":"MMT","metrics":{"text-to-video Median Rank":"21","text-to-video R@1":"13.2","text-to-video R@10":"38.8","text-to-video R@5":"29.2"},"uses_additional_data":false,"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":1,"n_unverified":6,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":31,"model":"CLIP","metrics":{"text-to-video Median Rank":"56.5","text-to-video R@1":"11.3","text-to-video R@10":"29.2","text-to-video R@5":"22.7","video-to-text Median Rank":"73","video-to-text R@1":"6.8","video-to-text R@10":"22.1","video-to-text R@5":"16.4"},"uses_additional_data":false,"paper_date":"2021-02-24","paper":"/paper/a-straightforward-framework-for-video","paper_url":"https://arxiv.org/abs/2102.12443v2","paper_title":"A Straightforward Framework For Video Retrieval Using CLIP","code":"https://github.com/Deferf/CLIP_Video_Representation","n_code_links":1,"syntology":null},{"rank_in_archive_order":32,"model":"Collaborative Experts","metrics":{"text-to-video Median Rank":"25","text-to-video R@1":"11.2","text-to-video R@10":"34.8","text-to-video R@5":"26.9"},"uses_additional_data":false,"paper_date":"2019-07-31","paper":"/paper/use-what-you-have-video-retrieval-using","paper_url":"https://arxiv.org/abs/1907.13487v2","paper_title":"Use What You Have: Video Retrieval Using Representations From Collaborative Experts","code":"https://github.com/albanie/collaborative-experts","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":1}},{"rank_in_archive_order":33,"model":"MoEE","metrics":{"text-to-video Median Rank":"27","text-to-video R@1":"10.1","text-to-video R@10":"34.6","text-to-video R@5":"25.6"},"uses_additional_data":true,"paper_date":"2018-04-07","paper":"/paper/learning-a-text-video-embedding-from","paper_url":"https://arxiv.org/abs/1804.02516v2","paper_title":"Learning a Text-Video Embedding from Incomplete and Heterogeneous Data","code":"https://github.com/jayleicn/TVRetrieval","n_code_links":5,"syntology":null},{"rank_in_archive_order":34,"model":"JSFusion","metrics":{"text-to-video Median Rank":"36","text-to-video R@1":"9.1","text-to-video R@10":"34.1","text-to-video R@5":"21.2"},"uses_additional_data":false,"paper_date":"2018-08-07","paper":"/paper/a-joint-sequence-fusion-model-for-video","paper_url":"http://arxiv.org/abs/1808.02559v1","paper_title":"A Joint Sequence Fusion Model for Video Question Answering and Retrieval","code":"https://github.com/antoine77340/howto100m","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":35,"model":"Large-Scale Discriminative Clustering","metrics":{"text-to-video Median Rank":"52","text-to-video R@1":"7.3","text-to-video R@10":"27.1","text-to-video R@5":"19.2"},"uses_additional_data":false,"paper_date":"2017-07-27","paper":"/paper/learning-from-video-and-text-via-large-scale","paper_url":"http://arxiv.org/abs/1707.09074v1","paper_title":"Learning from Video and Text via Large-Scale Discriminative Clustering","code":"https://github.com/jpeyre/unrel","n_code_links":2,"syntology":null},{"rank_in_archive_order":36,"model":"Text-Video Embedding","metrics":{"text-to-video Median Rank":"40","text-to-video R@1":"7.2","text-to-video R@10":"27.9","text-to-video R@5":"19.6"},"uses_additional_data":false,"paper_date":"2019-06-07","paper":"/paper/howto100m-learning-a-text-video-embedding-by","paper_url":"https://arxiv.org/abs/1906.03327v2","paper_title":"HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips","code":"https://github.com/antoine77340/MIL-NCE_HowTo100M","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":37,"model":"CT-SAN","metrics":{"text-to-video Median Rank":"46","text-to-video R@1":"5.1","text-to-video R@10":"25.2","text-to-video R@5":"16.3"},"uses_additional_data":false,"paper_date":"2016-10-10","paper":"/paper/end-to-end-concept-word-detection-for-video","paper_url":"http://arxiv.org/abs/1610.02947v3","paper_title":"End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":38,"model":"EMCL-Net (Ours)++ LSMDC Rohrbach et al. (2015)","metrics":{"text-to-video Mean Rank":"8","text-to-video R@10":"53.7"},"uses_additional_data":false,"paper_date":"2022-11-21","paper":"/paper/expectation-maximization-contrastive-learning","paper_url":"https://arxiv.org/abs/2211.11427v1","paper_title":"Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations","code":"https://github.com/jpthu17/emcl","n_code_links":4,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}}],"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 6,795 of the 9,581 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":9581,"papers_checked":6795,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2785},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+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":21,"rows_with_any_sample_ran":19,"distinct_papers_with_graph_line":18,"distinct_papers_with_any_sample_ran":16,"samples_over_distinct_papers":{"n_ran":47,"n_unverified":44,"n_samples":91,"n_pointer_only_licence":18,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":54,"n_unverified":52,"n_samples":106,"n_pointer_only_licence":18,"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"}}}