mlsanityData to 7 Oct 2026

Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis

arXiv 2502.07277 ↗First posted 11 Feb 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and recent advancements in deep neural networks in video understanding. We will review some of the main trends in video understanding models and their structural design, the main problems, and some offered solutions in this topic. We will also review and compare significant video understanding and action recognition datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 112 of its 171 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

112 of 171 references.

References · 112

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  7. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  8. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  9. 2020End-to-End Object Detection with Transformerscited by 7,384
  10. 2017Squeeze-and-Excitation Networkscited by 4,763
  11. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  12. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
38 more
  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  2. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  3. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  4. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  5. 2017Non-local Neural Networkscited by 2,290
  6. 2017The Kinetics Human Action Video Datasetcited by 1,894
  7. 2018SlowFast Networks for Video Recognitioncited by 1,862
  8. 2020Reformer: The Efficient Transformercited by 1,658
  9. 2021ViViT: A Video Vision Transformercited by 1,610
  10. 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
  11. 2020Rethinking Attention with Performerscited by 1,584
  12. 2017Deformable Convolutional Networkscited by 1,481
  13. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  14. 2020Point Transformercited by 1,462
  15. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  16. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
  17. 2017The "something something" video database for learning and evaluating visual common sensecited by 1,092
  18. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  19. 2021Transformers in Vision: A Surveycited by 1,009
  20. 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
  21. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 899
  22. 2021Multiscale Vision Transformerscited by 896
  23. 2020Pre-Trained Image Processing Transformercited by 891
  24. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  25. 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 852
  26. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  27. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  28. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 717
  29. 2019VideoBERT: A Joint Model for Video and Language Representation Learningcited by 702
  30. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
  31. 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 635
  32. 2016Hollywood in Homes: Crowdsourcing Data Collection for Activity Understandingcited by 535
  33. 2021CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windowscited by 526
  34. 2017Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classificationcited by 521
  35. 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 483
  36. 2021Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attentioncited by 468
  37. 2017AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actionscited by 449
  38. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411

50 most cited of 112 All 112 (JSON)

Built on by

Not cited yet.