{"url":"/sota/video-retrieval-on-msr-vtt","task":{"name":"Video Retrieval","url":"/task/video-retrieval","note":null},"dataset":{"name":"MSR-VTT","url":"/dataset/msr-vtt"},"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 Mean Rank","text-to-video Median Rank","video-to-text R@1","video-to-text R@5","video-to-text R@10","video-to-text Median Rank","video-to-text Mean Rank","text-to-video MedianR","text-to-videoMedian 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 Mean Rank":null,"text-to-video Median 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,"video-to-text Mean Rank":null,"text-to-video MedianR":null,"text-to-videoMedian Rank":null}},"counts":{"rows":40,"rows_with_code":30,"rows_with_paper_page":39,"rows_dated":39,"rows_using_additional_data":21},"rows":[{"rank_in_archive_order":1,"model":"GRAM","metrics":{"text-to-video R@1":"64","text-to-video R@10":"89.3","video-to-text R@1":"64.8","video-to-text R@10":"91.5"},"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":2,"model":"VAST","metrics":{"text-to-video R@1":"63.9","text-to-video R@10":"89.6","text-to-video R@5":"84.3"},"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":3,"model":"InternVideo2-6B","metrics":{"text-to-video R@1":"62.8","video-to-text R@1":"60.2"},"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":"VALOR","metrics":{"text-to-video R@1":"59.9","text-to-video R@10":"89.6","text-to-video R@5":"83.5"},"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":5,"model":"UMT-L (ViT-L/16)","metrics":{"text-to-video R@1":"58.8","text-to-video R@10":"87.1","text-to-video R@5":"81.0","video-to-text R@1":"58.6","video-to-text R@10":"86.5","video-to-text R@5":"81.6"},"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":6,"model":"vid-TLDR (UMT-L)","metrics":{"text-to-video R@1":"58.1","text-to-video R@10":"81.6","text-to-video R@5":"81.0","video-to-text R@1":"58.7","video-to-text R@10":"86.9","video-to-text R@5":"81.6"},"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":7,"model":"COSA","metrics":{"text-to-video R@1":"57.9"},"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":8,"model":"InternVideo","metrics":{"text-to-video R@1":"55.2","video-to-text R@1":"57.9"},"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":"VLAB","metrics":{"text-to-video R@1":"55.1","text-to-video R@10":"87.6","text-to-video R@5":"78.8"},"uses_additional_data":true,"paper_date":"2023-05-22","paper":"/paper/vlab-enhancing-video-language-pre-training-by","paper_url":"https://arxiv.org/abs/2305.13167v1","paper_title":"VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"Aurora (ours, r=64)","metrics":{"text-to-video R@1":"52.4","text-to-video R@10":"82","text-to-video R@5":"73.9","text-to-videoMedian Rank":"1"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"TEFAL","metrics":{"text-to-video R@1":"52","text-to-video R@10":"86.1","text-to-video R@5":"76.6"},"uses_additional_data":false,"paper_date":"2023-07-24","paper":"/paper/audio-enhanced-text-to-video-retrieval-using","paper_url":"https://arxiv.org/abs/2307.12964v2","paper_title":"Audio-Enhanced Text-to-Video Retrieval using Text-Conditioned Feature Alignment","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":12,"model":"UCoFiA","metrics":{"text-to-video R@1":"49.4","text-to-video R@10":"83.5","text-to-video R@5":"72.1"},"uses_additional_data":false,"paper_date":"2023-09-18","paper":"/paper/unified-coarse-to-fine-alignment-for-video","paper_url":"https://arxiv.org/abs/2309.10091v1","paper_title":"Unified Coarse-to-Fine Alignment for Video-Text Retrieval","code":"https://github.com/ziyang412/ucofia","n_code_links":1,"syntology":{"n_ran":8,"n_unverified":6,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"OmniVL","metrics":{"text-to-video R@1":"47.8","text-to-video R@10":"83.8","text-to-video R@5":"74.2"},"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":14,"model":"CLIP4Clip-seqTransf","metrics":{"text-to-video R@1":"44.5","text-to-video R@10":"81.6","text-to-video R@5":"71.4"},"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":15,"model":"All-in-one + MELTR","metrics":{"text-to-video R@1":"38.6","text-to-video R@10":"84.7","text-to-video R@5":"74.4"},"uses_additional_data":true,"paper_date":"2023-03-23","paper":"/paper/meltr-meta-loss-transformer-for-learning-to","paper_url":"https://arxiv.org/abs/2303.13009v1","paper_title":"MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models","code":"https://github.com/mlvlab/MELTR","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":16,"model":"VIOLETv2","metrics":{"text-to-video R@1":"37.2","text-to-video R@10":"75.8","text-to-video R@5":"64.8"},"uses_additional_data":true,"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":17,"model":"HD-VILA","metrics":{"text-to-video MedianR":"3","text-to-video R@1":"35.6","text-to-video R@10":"78","text-to-video R@5":"65.3"},"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":18,"model":"VideoCoCa (zero-shot)","metrics":{"text-to-video R@1":"34.3","text-to-video R@10":"67.0","text-to-video R@5":"57.8","video-to-text R@1":"64.7","video-to-text R@10":"91.4","video-to-text R@5":"85.2"},"uses_additional_data":true,"paper_date":"2022-12-09","paper":"/paper/video-text-modeling-with-zero-shot-transfer","paper_url":"https://arxiv.org/abs/2212.04979v3","paper_title":"VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"MDMMT-2","metrics":{"text-to-video Mean Rank":"37.8","text-to-video Median Rank":"3.0","text-to-video R@1":"33.7","text-to-video R@10":"70.8","text-to-video R@5":"60.5"},"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":20,"model":"VIOLET + MELTR","metrics":{"text-to-video Median Rank":"3","text-to-video R@1":"33.6","text-to-video R@10":"77.8","text-to-video R@5":"63.7"},"uses_additional_data":false,"paper_date":"2023-03-23","paper":"/paper/meltr-meta-loss-transformer-for-learning-to","paper_url":"https://arxiv.org/abs/2303.13009v1","paper_title":"MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models","code":"https://github.com/mlvlab/MELTR","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":21,"model":"CLIP2TV","metrics":{"text-to-video Mean Rank":"44.7","text-to-video Median Rank":"3","text-to-video R@1":"33.1","text-to-video R@10":"68.9","text-to-video R@5":"58.9"},"uses_additional_data":true,"paper_date":"2021-11-10","paper":"/paper/clip2tv-an-empirical-study-on-transformer","paper_url":"https://arxiv.org/abs/2111.05610v2","paper_title":"CLIP2TV: Align, Match and Distill for Video-Text Retrieval","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"CAMoE","metrics":{"text-to-video Mean Rank":"42.6","text-to-video Median Rank":"3","text-to-video R@1":"32.9","text-to-video R@10":"68.4","text-to-video R@5":"58.3","video-to-text Mean Rank":"3.8","video-to-text Median Rank":"1","video-to-text R@1":"59.8","video-to-text R@10":"92.8","video-to-text R@5":"86.2"},"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":23,"model":"FROZEN","metrics":{"text-to-video R@1":"32.5","text-to-video R@10":"71.2","text-to-video R@5":"61.5"},"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":3,"n_unverified":8,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":24,"model":"COTS","metrics":{"text-to-video Median Rank":"3","text-to-video R@1":"32.1","text-to-video R@10":"70.2","text-to-video R@5":"60.8"},"uses_additional_data":false,"paper_date":"2022-04-15","paper":"/paper/cots-collaborative-two-stream-vision-language","paper_url":"https://arxiv.org/abs/2204.07441v2","paper_title":"COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":25,"model":"CoCa (zero-shot)","metrics":{"text-to-video R@1":"30.0","text-to-video R@10":"61.6","text-to-video R@5":"52.4","video-to-text R@1":"49.9","video-to-text R@10":"81.4","video-to-text R@5":"73.4"},"uses_additional_data":true,"paper_date":"2022-05-04","paper":"/paper/coca-contrastive-captioners-are-image-text","paper_url":"https://arxiv.org/abs/2205.01917v2","paper_title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","code":"https://github.com/mlfoundations/open_clip","n_code_links":6,"syntology":{"n_ran":9,"n_unverified":8,"n_samples":17,"n_pointer_only_licence":0}},{"rank_in_archive_order":26,"model":"CLIP2Video","metrics":{"text-to-video Mean Rank":"45.4","text-to-video Median Rank":"4","text-to-video R@1":"29.8","text-to-video R@10":"66.2","text-to-video R@5":"55.5","video-to-text Mean Rank":"5.3","video-to-text Median Rank":"1","video-to-text R@1":"54.6","video-to-text R@10":"90.8","video-to-text R@5":"82.1"},"uses_additional_data":true,"paper_date":"2021-06-21","paper":"/paper/clip2video-mastering-video-text-retrieval-via","paper_url":"https://arxiv.org/abs/2106.11097v1","paper_title":"CLIP2Video: Mastering Video-Text Retrieval via Image CLIP","code":"https://github.com/CryhanFang/CLIP2Video","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":27,"model":"LAFF","metrics":{"text-to-video R@1":"29.1","text-to-video R@10":"65.8","text-to-video R@5":"54.9"},"uses_additional_data":false,"paper_date":"2021-12-03","paper":"/paper/lightweight-attentional-feature-fusion-for","paper_url":"https://arxiv.org/abs/2112.01832v3","paper_title":"Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval","code":"https://github.com/ruc-aimc-lab/laff","n_code_links":1,"syntology":null},{"rank_in_archive_order":28,"model":"UniVL + MELTR","metrics":{"text-to-video Median Rank":"4","text-to-video R@1":"28.5","text-to-video R@10":"67.6","text-to-video R@5":"55.5"},"uses_additional_data":false,"paper_date":"2023-03-23","paper":"/paper/meltr-meta-loss-transformer-for-learning-to","paper_url":"https://arxiv.org/abs/2303.13009v1","paper_title":"MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models","code":"https://github.com/mlvlab/MELTR","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":29,"model":"Ours","metrics":{"text-to-video Median Rank":"3","text-to-video R@1":"26","text-to-video R@5":"56.7","video-to-text Median Rank":"3","video-to-text R@1":"26.7","video-to-text R@5":"56.5"},"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":30,"model":"TACo","metrics":{"text-to-video Median Rank":"5","text-to-video R@1":"24.8","text-to-video R@10":"64.0","text-to-video R@5":"52.1"},"uses_additional_data":true,"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":31,"model":"MDMMT","metrics":{"text-to-video Mean Rank":"52.8","text-to-video Median Rank":"6","text-to-video R@1":"23.1","text-to-video R@10":"61.8","text-to-video R@5":"49.8"},"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":32,"model":"CLIP","metrics":{"text-to-video Median Rank":"10","text-to-video R@1":"21.4","text-to-video R@10":"50.4","text-to-video R@5":"41.1","video-to-text Median Rank":"2","video-to-text R@1":"40.3","video-to-text R@10":"79.2","video-to-text R@5":"69.7"},"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":33,"model":"UniVL","metrics":{"text-to-video Median Rank":"6","text-to-video R@1":"21.2","text-to-video R@10":"63.1","text-to-video R@5":"49.6"},"uses_additional_data":true,"paper_date":"2020-02-15","paper":"/paper/univilm-a-unified-video-and-language-pre","paper_url":"https://arxiv.org/abs/2002.06353v3","paper_title":"UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation","code":"https://github.com/microsoft/UniVL","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":1}},{"rank_in_archive_order":34,"model":"Text-Video Embedding","metrics":{"text-to-video Median Rank":"9","text-to-video R@1":"14.9","text-to-video R@10":"52.8","video-to-text R@5":"40.2"},"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":35,"model":"RoME","metrics":{"text-to-video Median Rank":"17","text-to-video R@1":"10.7","text-to-video R@10":"41.2","text-to-video R@5":"29.6"},"uses_additional_data":false,"paper_date":"2022-06-26","paper":"/paper/rome-role-aware-mixture-of-expert-transformer","paper_url":"https://arxiv.org/abs/2206.12845v1","paper_title":"RoME: Role-aware Mixture-of-Expert Transformer for Text-to-Video Retrieval","code":"https://github.com/buraksatar/RoME_video_retrieval","n_code_links":1,"syntology":null},{"rank_in_archive_order":36,"model":"JSFusion","metrics":{"text-to-video Median Rank":"13","text-to-video R@1":"10.2","text-to-video R@10":"43.2","video-to-text R@5":"31.2"},"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":37,"model":"Collaborative Experts","metrics":{"text-to-video Mean Rank":"86.8","text-to-video Median Rank":"16","text-to-video R@1":"10.0","text-to-video R@10":"41.2","text-to-video R@5":"29.0","video-to-text Mean Rank":"38.1","video-to-text Median Rank":"8.3","video-to-text R@1":"15.6","video-to-text R@10":"55.2","video-to-text R@5":"40.9"},"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":38,"model":"JEMC","metrics":{"text-to-video Mean Rank":"213.8","text-to-video Median Rank":"29.7","text-to-video R@1":"7.0","text-to-video R@10":"29.7","text-to-video R@5":"20.9","video-to-text Mean Rank":"134","video-to-text Median Rank":"16","video-to-text R@1":"12.5","video-to-text R@10":"42.2","video-to-text R@5":"32.1"},"uses_additional_data":false,"paper_date":"2018-06-11","paper":"/paper/learning-joint-embedding-with-multimodal-cues","paper_url":"https://dl.acm.org/citation.cfm?id=3206064","paper_title":"Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval","code":"https://github.com/niluthpol/multimodal_vtt","n_code_links":1,"syntology":null},{"rank_in_archive_order":39,"model":"Kaufman","metrics":{"text-to-video Median Rank":"41","text-to-video R@1":"4.7","text-to-video R@10":"24.1","video-to-text R@5":"16.6"},"uses_additional_data":false,"paper_date":"2016-12-21","paper":"/paper/temporal-tessellation-a-unified-approach-for","paper_url":"http://arxiv.org/abs/1612.06950v2","paper_title":"Temporal Tessellation: A Unified Approach for Video Analysis","code":"https://github.com/dot27/temporal-tessellation","n_code_links":1,"syntology":null},{"rank_in_archive_order":40,"model":"C+LSTM+SA+FC7","metrics":{"text-to-video Median Rank":"55","text-to-video R@1":"4.2","text-to-video R@10":"19.9","video-to-text R@5":"12.9"},"uses_additional_data":false,"paper_date":"2016-09-26","paper":"/paper/learning-language-visual-embedding-for-movie","paper_url":"http://arxiv.org/abs/1609.08124v1","paper_title":"Learning Language-Visual Embedding for Movie Understanding with Natural-Language","code":null,"n_code_links":0,"syntology":null}],"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 6,264 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":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"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":18,"rows_with_any_sample_ran":16,"distinct_papers_with_graph_line":16,"distinct_papers_with_any_sample_ran":14,"samples_over_distinct_papers":{"n_ran":57,"n_unverified":77,"n_samples":134,"n_pointer_only_licence":11,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":59,"n_unverified":77,"n_samples":136,"n_pointer_only_licence":11,"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"}}}