A curated list of deep learning resources for video-text retrieval.

Please feel free to pull requests to add papers.

Markdown format:

- `[Author Journal/Booktitle Year]` Title. Journal/Booktitle, Year. [[paper]](link) [[code]](link) [[homepage]](link)

• Implementations
  • PyTorch
  • TensorFlow
  • Others
• Papers
  • 2023 - 2022 - 2021 - 2020 - 2019 - 2018 - Before
  • Ad-hoc Video Search
  • Other Related
• Datasets

• hybrid_space
• dual_encoding
• w2vvpp
• Mixture-of-Embedding-Experts
• howto100m
• collaborative
• hgr
• coot
• mmt
• ClipBERT

• jsfusion

• w2vv(Keras)

• Extracting CNN features from video frames by MXNet

• [Pei et al. CVPR23] CLIPPING: Distilling CLIP-Based Models with a Student Base for Video-Language Retrieval. CVPR, 2023. [paper]
• [Li et al. CVPR23] SViTT: Temporal Learning of Sparse Video-Text Transformers. CVPR, 2023. [paper] [code]
• [Wu et al. CVPR23] Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval. CVPR, 2023. [paper] [code]
• [Ko et al. CVPR23] MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models. CVPR, 2023. [paper] [code]
• [Wang et al. CVPR23] All in One: Exploring Unified Video-Language Pre-Training. CVPR, 2023. [paper] [code]
• [Girdhar et al. CVPR23] IMAGEBIND: One Embedding Space To Bind Them All. CVPR, 2023. [paper] [code]
• [Huang et al. CVPR23] VoP: Text-Video Co-Operative Prompt Tuning for Cross-Modal Retrieval. CVPR, 2023. [paper] [code]
• [Li et al. CVPR23] LAVENDER: Unifying Video-Language Understanding As Masked Language Modeling. CVPR, 2023. [paper] [code]
• [Huang et al. CVPR23] Clover: Towards a Unified Video-Language Alignment and Fusion Model. CVPR, 2023. [paper] [code]
• [Ji et al. CVPR23] Seeing What You Miss: Vision-Language Pre-Training With Semantic Completion Learning. CVPR, 2023. [paper]
• [Gan et al. CVPR23] CNVid-3.5M: Build, Filter, and Pre-train the Large-scale Public Chinese Video-text Dataset. CVPR, 2023. [paper] [code]
• [Zhao et al. CVPRW23] Cali-NCE: Boosting Cross-Modal Video Representation Learning With Calibrated Alignment. CVPRWorkshop, 2023. [paper]
• [Ma et al. TCSVT23] Using Multimodal Contrastive Knowledge Distillation for Video-Text Retrieval. TCSVT, 2023. [paper]

• [Dong et al. ACMMM22] Partially Relevant Video Retrieval. ACM Multimedia, 2022. [homepage] [paper] [code] A new text-to-video retrieval subtask
• [Wang et al. ACMMM22] Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning. ACM Multimedia, 2022. [paper] [code]
• [Wang et al. ACMMM22] Learn to Understand Negation in Video Retrieval. ACM Multimedia, 2022. [paper] [code]
• [Falcon et al. ACMMM22] A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval. ACM Multimedia, 2022. [paper] [code]
• [Ma et al. ACMMM22] X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. ACM Multimedia, 2022. [paper]
• [Hu et al. ECCV22] Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval. ECCV, 2022. [paper] [code]
• [Liu et al. ECCV22] TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval. ECCV, 2022. [paper] [code]
• [Dong et al. TCSVT22] Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval. TCSVT, 2022. [paper] [code]
• [Li et al. CVPR22] Align and Prompt: Video-and-Language Pre-training with Entity Prompts, CVPR, 2022. [paper] [code]
• [Shvetsova et al. CVPR22]Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval. CVPR, 2022. [paper] [code]
• [Ge et al. CVPR22]Bridging Video-text Retrieval with Multiple Choice Questions. CVPR, 2022. [paper] [code]
• [Han et al. CVPR22]Temporal Alignment Networks for Long-term Video. CVPR.2022. [paper] [code]
• [Gorti et al. CVPR22] X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval. CVPR, 2022. [paper] [code]
• [Lu et al. NIPS22] LGDN: Language-Guided Denoising Network for Video-Language Modeling. NIPS, 2022. [paper]
• [Liu et al. SIGIR22] Animating Images to Transfer CLIP for Video-Text Retrieval. SIGIR, 2022. [paper]
• [Zhao et al. SIGIR22] CenterCLIP: Token Clustering for Efficient Text-Video Retrieval. SIGIR, 2022. [paper]
• [Liu et al. ACL22] Cross-Modal Discrete Representation Learning. ACL, 2022. [paper]
• [Gabeur et al. WACV22] Masking Modalities for Cross-modal Video Retrieval. WACV, 2022. [paper]
• [Cao et al. AAAI22] Visual Consensus Modeling for Video-Text Retrieval. AAAI, 2022. [paper] [code]
• [Cheng et al. AAAI22] Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss. AAAI, 2022. [paper][code]
• [Wang et al. TMM22] Many Hands Make Light Work: Transferring Knowledge from Auxiliary Tasks for Video-Text Retrieval. IEEE Transactions on Multimedia, 2022. [paper]
• [Park et al. NAACL22] Exposing the Limits of Video-Text Models through Contrast Sets. NAACL, 2022. [paper] [code]
• [Song et al. TOMM22] Adversarial Multi-Grained Embedding Network for Cross-Modal Text-Video Retrieval. TOMM, 2022. [paper]
• [Bai et al. ARXIV22] LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval. arXiv:2207.04858, 2022. [paper]
• [Bain et al. ARXIV22] A CLIP-Hitchhiker's Guide to Long Video Retrieval. arXiv:2205.08508, 2022. [paper]
• [Gao et al. ARXIV22] CLIP2TV: Align, Match and Distill for Video-Text Retrieval. arXiv:2111.05610, 2022. [paper]
• [Jiang et al. ARXIV22] Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations. arXiv:2204.03382, 2022. [paper]

• [Dong et al. TPAMI21] Dual Encoding for Video Retrieval by Text. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [paper] [code]
• [Wei et al. TPAMI21] Universal Weighting Metric Learning for Cross-Modal Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [paper]
• [Lei et al. CVPR21] Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling. CVPR, 2021. [paper] [code]
• [Wray et al. CVPR21] On Semantic Similarity in Video Retrieval. CVPR, 2021. [paper] [code]
• [Chen et al. CVPR21] Learning the Best Pooling Strategy for Visual Semantic Embedding. CVPR, 2021. [paper][code]
• [Wang et al. CVPR21] T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval. CVPR, 2021. [paper]
• [Miech et al. CVPR21] Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers. CVPR, 2021. [paper]
• [Liu et al. CVPR21] Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval. CVPR, 2021. [paper]
• [Chen et al. ICCV21] Multimodal Clustering Networks for Self-Supervised Learning from Unlabeled Videos. ICCV, 2021. [paper]
• [Ioana et al. ICCV21] TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval. ICCV, 2021. [paper][code]
• [Yang et al. ICCV21] TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment. ICCV, 2021. [paper]
• [Bian et al. ICCV21] Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval. ICCV, 2021. [paper][code]
• [Wen et al. ICCV21] COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-Training for Vision-Language Representation. ICCV, 2021. [paper][code]
• [Luo et al. ACMMM21] CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising. ACM Multimedia, 2021. [paper]
• [Wu et al. ACMMM21] HANet: Hierarchical Alignment Networks for Video-Text Retrieval. ACM Multimedia, 2021. [paper][code]
• [Liu et al. ACMMM21] Progressive Semantic Matching for Video-Text Retrieval. ACM Multimedia, 2021. [paper]
• [Han et al. ACMMM21] Fine-grained Cross-modal Alignment Network for Text-Video Retrieval. ACM Multimedia, 2021. [paper]
• [Wei et al. ACMMM21] Meta Self-Paced Learning for Cross-Modal Matching. ACM Multimedia, 2021. [paper]
• [Patrick et al. ICLR21] Support-set Bottlenecks for Video-text Representation Learning. ICLR, 2021. [paper]
• [Qi et al. TIP21] Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval. IEEE Transactions on Image Processing, 2021. [paper]
• [Song et al. TMM21] Spatial-temporal Graphs for Cross-modal Text2Video Retrieval. IEEE Transactions on Multimedia, 2021. [paper]
• [Dong et al. NEUCOM21] Multi-level Alignment Network for Domain Adaptive Cross-modal Retrieval. Neurocomputing, 2021. [paper] [code]
• [Jin et al. SIGIR21] Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval. SIGIR, 2020. [paper]
• [He et al. SIGIR21]Improving Video Retrieval by Adaptive Margin. SIGIR, 2021. [paper]
• [Wang et al. IJCAI21] Dig into Multi-modal Cues for Video Retrieval with Hierarchical Alignment. IJCAI, 2021. [paper]
• [Chen et al. AAAI21] Mind-the-Gap! Unsupervised Domain Adaptation for Text-Video Retrieval. AAAI, 2021. [paper]
• [Hao et al. ICME21]What Matters: Attentive and Relational Feature Aggregation Network for Video-Text Retrieval. ICME, 2021. [paper]
• [Wu et al. ICME21]Multi-Dimensional Attentive Hierarchical Graph Pooling Network for Video-Text Retrieval. ICME, 2021. [paper]
• [Song et al. ICIP21] Semantic-Preserving Metric Learning for Video-Text Retrieval. IEEE International Conference on Image Processing, 2021. [paper]
• [Hao et al. ICMR21] Multi-Feature Graph Attention Network for Cross-Modal Video-Text Retrieval. ICMR, 2021. [paper]
• [Liu et al. ARXIV21] HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval. arXiv:2103.15049, 2021. [paper]
• [Akbari et al. ARXIV21] VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text. arXiv:2104.11178 , 2021. [paper] [code]
• [Fang et al. ARXIV21] CLIP2Video: Mastering Video-Text Retrieval via Image CLIP. arXiv:2106.11097, 2021. [paper] [code]
• [Luo et al. ARXIV21] CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval. arXiv:2104.08860, 2021. [paper][code]
• [Li et al. ARXIV21] Align and Prompt: Video-and-Language Pre-training with Entity Prompts. arXiv:2112.09583, 2021. [paper][code]

• [Yang et al. SIGIR20] Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval. SIGIR, 2020. [paper]
• [Ging et al. NeurIPS20] COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning. NeurIPS, 2020. [paper] [code]
• [Gabeur et al. ECCV20] Multi-modal Transformer for Video Retrieval. ECCV, 2020. [paper] [code] [homepage]
• [Li et al. TMM20] SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries. IEEE Transactions on Multimedia, 2020. [paper]
• [Wang et al. TMM20] Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval. IEEE Transactions on Multimedia, 2020. [paper]
• [Chen et al. TMM20] Interclass-Relativity-Adaptive Metric Learning for Cross-Modal Matching and Beyond. IEEE Transactions on Multimedia, 2020. [paper]
• [Wu et al. ACMMM20] Interpretable Embedding for Ad-Hoc Video Search. ACM Multimedia, 2020. [paper]
• [Feng et al. IJCAI20] Exploiting Visual Semantic Reasoning for Video-Text Retrieval. IJCAI, 2020. [paper]
• [Wei et al. CVPR20] Universal Weighting Metric Learning for Cross-Modal Retrieval. CVPR, 2020. [paper]
• [Doughty et al. CVPR20] Action Modifiers: Learning from Adverbs in Instructional Videos. CVPR, 2020. [paper]
• [Chen et al. CVPR20] Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning. CVPR, 2020. [paper]
• [Zhu et al. CVPR20] ActBERT: Learning Global-Local Video-Text Representations. CVPR, 2020. [paper]
• [Miech et al. CVPR20] End-to-End Learning of Visual Representations From Uncurated Instructional Videos. CVPR, 2020. [paper] [code] [homepage]
• [Zhao et al. ICME20] Stacked Convolutional Deep Encoding Network For Video-Text Retrieval. ICME, 2020. [paper]
• [Luo et al. ARXIV20] UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation. arXiv:2002.06353, 2020. [paper]

• [Dong et al. CVPR19] Dual Encoding for Zero-Example Video Retrieval. CVPR, 2019. [paper] [code]
• [Song et al. CVPR19] Polysemous visual-semantic embedding for cross-modal retrieval. CVPR, 2019. [paper]
• [Wray et al. ICCV19] Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings. ICCV, 2019. [paper]
• [Xiong et al. ICCV19] A Graph-Based Framework to Bridge Movies and Synopses. ICCV, 2019. [paper]
• [Li et al. ACMMM19] W2VV++ Fully Deep Learning for Ad-hoc Video Search. ACM Multimedia, 2019. [paper] [code]
• [Liu et al. BMVC19] Use What You Have: Video Retrieval Using Representations From Collaborative Experts. MBVC, 2019. [paper] [code]
• [Choi et al. BigMM19] From Intra-Modal to Inter-Modal Space: Multi-Task Learning of Shared Representations for Cross-Modal Retrieval. International Conference on Multimedia Big Data, 2019. [paper]

• [Dong et al. TMM18] Predicting visual features from text for image and video caption retrieval. IEEE Transactions on Multimedia, 2018. [paper] [code]
• [Zhang et al. ECCV18] Cross-Modal and Hierarchical Modeling of Video and Text. ECCV, 2018. [paper] [code]
• [Yu et al. ECCV18] A Joint Sequence Fusion Model for Video Question Answering and Retrieval. ECCV, 2018. [paper]
• [Shao et al. ECCV18] Find and focus: Retrieve and localize video events with natural language queries. ECCV, 2018. [paper]
• [Mithun et al. ICMR18] Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval. ICMR, 2018. [paper] [code]
• [Miech et al. arXiv18] Learning a Text-Video Embedding from Incomplete and Heterogeneous Data. arXiv preprint arXiv:1804.02516, 2018. [paper] [code]

• [Yu et al. CVPR17] End-to-end concept word detection for video captioning, retrieval, and question answering. CVPR, 2017. [paper] [code]
• [OtaniEmail et al. ECCVW2016] Learning joint representations of videos and sentences with web image search. ECCV Workshop, 2016. [paper]
• [Xu et al. AAAI15] Jointly modeling deep video and compositional text to bridge vision and language in a unified framework. AAAI, 2015. [paper]

• For the papers targeting at ad-hoc video search in the context of TRECVID, please refer to here.

• [Rouditchenko et al. INTERSPEECH21] AVLnet: Learning Audio-Visual Language Representations from Instructional Videos. Interspeech, 2021. [paper] [code]
• [Li et al. arXiv20] Learning Spatiotemporal Features via Video and Text Pair Discrimination. arXiv preprint arXiv:2001.05691, 2020. [paper]

• [MSVD] David et al. Collecting Highly Parallel Data for Paraphrase Evaluation. ACL, 2011. [paper] [dataset]
• [MSRVTT] Xu et al. MSR-VTT: A Large Video Description Dataset for Bridging Video and Language. CVPR, 2016. [paper] [dataset]
• [TGIF] Li et al. TGIF: A new dataset and benchmark on animated GIF description. CVPR, 2016. [paper] [homepage]
• [AVS] Awad et al. Trecvid 2016: Evaluating video search, video event detection, localization, and hyperlinking. TRECVID Workshop, 2016. [paper] [dataset]
• [LSMDC] Rohrbach et al. Movie description. IJCV, 2017. [paper] [dataset]
• [ActivityNet Captions] Krishna et al. Dense-captioning events in videos. ICCV, 2017. [paper] [dataset]
• [DiDeMo] Hendricks et al. Localizing Moments in Video with Natural Language. ICCV, 2017. [paper] [code]
• [HowTo100M] Miech et al. HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips. ICCV, 2019. [paper] [homepage]
• [VATEX] Wang et al. VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research. ICCV, 2019. [paper] [homepage]

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