mlsanityData to 7 Oct 2026

Two-Stream Convolutional Networks for Action Recognition in Videos

arXiv 1406.2199 ↗First posted 9 Jun 2014cs.CVcited by 1,067

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Cited by 28 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multi-task learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification.

Key facts. Cited by 1,067 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2014.

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Citing papers per year: 2021: 289; 2022: 260; 2023: 191; 2024: 138; 2025: 112; 2026: 77.

1,067 papers.

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  1. 2022A ConvNet for the 2020scited by 3,545
  2. 2021ViViT: A Video Vision Transformercited by 1,610
  3. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
  4. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,265
  5. 2021Multiscale Vision Transformerscited by 896
  6. 2021NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actionscited by 735
  7. 2021CLIPort: What and Where Pathways for Robotic Manipulationcited by 617
  8. 2024Latte: Latent Diffusion Transformer for Video Generationcited by 536
  9. 2021MViTv2: Improved Multiscale Vision Transformers for Classification and Detectioncited by 516
  10. 2021Less is More: ClipBERT for Video-and-Language Learning via Sparse Samplingcited by 484
  11. 2023LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignmentcited by 366
  12. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
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  1. 2024VideoMamba: State Space Model for Efficient Video Understandingcited by 325
  2. 2021Prompting Visual-Language Models for Efficient Video Understandingcited by 309
  3. 2022ActionFormer: Localizing Moments of Actions with Transformerscited by 308
  4. 2022Unifying Flow, Stereo and Depth Estimationcited by 270
  5. 2021Revisiting Skeleton-based Action Recognitioncited by 256
  6. 2021H2O: Two Hands Manipulating Objects for First Person Interaction Recognitioncited by 251
  7. 2021Anabranch Network for Camouflaged Object Segmentationcited by 227
  8. 2022Omnivore: A Single Model for Many Visual Modalitiescited by 214
  9. 2021Anticipative Video Transformercited by 197
  10. 2022Frozen CLIP Models are Efficient Video Learnerscited by 181
  11. 2021Improving Sign Language Translation with Monolingual Data by Sign Back-Translationcited by 177
  12. 2021BEVT: BERT Pretraining of Video Transformerscited by 169
  13. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  14. 2022Revisiting the "Video" in Video-Language Understandingcited by 167
  15. 2021Learning Salient Boundary Feature for Anchor-free Temporal Action Localizationcited by 156
  16. 2021E-RAFT: Dense Optical Flow from Event Camerascited by 149
  17. 2022Multiview Transformers for Video Recognitioncited by 148
  18. 2021Towards Long-Form Video Understandingcited by 146
  19. 2021MoViNets: Mobile Video Networks for Efficient Video Recognitioncited by 135
  20. 2021TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?cited by 127
  21. 2024SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Modelscited by 123
  22. 2022Two-Stream Network for Sign Language Recognition and Translationcited by 122
  23. 2022Revealing the Dark Secrets of Masked Image Modelingcited by 117
  24. 2021Relaxed Transformer Decoders for Direct Action Proposal Generationcited by 116
  25. 2022Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)cited by 115
  26. 2021Learning Accurate Dense Correspondences and When to Trust Themcited by 113
  27. 2021Temporal Context Aggregation Network for Temporal Action Proposal Refinementcited by 111
  28. 2021Broaden Your Views for Self-Supervised Video Learningcited by 108
  29. 2022IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolationcited by 107
  30. 2022Long Movie Clip Classification with State-Space Video Modelscited by 106
  31. 2022FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessmentcited by 106
  32. 2023Selective Structured State-Spaces for Long-Form Video Understandingcited by 105
  33. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  34. 2022OmniMAE: Single Model Masked Pretraining on Images and Videoscited by 99
  35. 2021VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluationcited by 95
  36. 2022Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learningcited by 90
  37. 2021An Image is Worth 16x16 Words, What is a Video Worth?cited by 87
  38. 2024Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Modelscited by 87

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