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
  • 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

• [Dong et al. TPAMI21] Dual Encoding for Video Retrieval by Text. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [paper] [code]
• [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]
• [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]
• [Dong et al. NEUCOM21] Multi-level Alignment Network for Domain Adaptive Cross-modal Retrieval. Neurocomputing, 2020. [paper]

• [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]
• [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.

• [Li et al. arXiv20] Learning Spatiotemporal Features via Video and Text Pair Discrimination. arXiv preprint arXiv:2001.05691, 2020. [paper]
• [Miech et al. CVPR20] End-to-End Learning of Visual Representations from Uncurated Instructional Videos. CVPR, 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]

To the extent possible under law, danieljf24 all copyright and related or neighboring rights to this repository.