# Going Deeper into Action Recognition: A Survey

arXiv: https://arxiv.org/abs/1605.04988 · HTML: https://mlsanity.com/paper/1605.04988/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2016.

## Built on by (most cited first, 30 of 55)

- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [Expanding Language-Image Pretrained Models for General Video Recognition](https://mlsanity.com/paper/2208.02816/) (2022)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Baichuan-Omni-1.5 Technical Report](https://mlsanity.com/paper/2501.15368/) (2025)
- [Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation](https://mlsanity.com/paper/2103.11264/) (2021)
- [Panoptic Video Scene Graph Generation](https://mlsanity.com/paper/2311.17058/) (2023)
- [Core Knowledge Deficits in Multi-Modal Language Models](https://mlsanity.com/paper/2410.10855/) (2024)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2404.05392/) (2024)
- [Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning](https://mlsanity.com/paper/2308.04828/) (2023)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Skeleton-based Action Recognition via Temporal-Channel Aggregation](https://mlsanity.com/paper/2205.15936/) (2022)
- [Few-Shot Action Recognition with Compromised Metric via Optimal Transport](https://mlsanity.com/paper/2104.03737/) (2021)
- [Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2211.10412/) (2022)
- [Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection](https://mlsanity.com/paper/2409.09953/) (2024)
- [POV: Prompt-Oriented View-Agnostic Learning for Egocentric Hand-Object Interaction in the Multi-View World](https://mlsanity.com/paper/2403.05856/) (2024)
- [Vision Language Models See What You Want but not What You See](https://mlsanity.com/paper/2410.00324/) (2024)
- [Grounding Partially-Defined Events in Multimodal Data](https://mlsanity.com/paper/2410.05267/) (2024)
- [Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset](https://mlsanity.com/paper/2407.04190/) (2024)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [From CNNs to Transformers in Multimodal Human Action Recognition: A Survey](https://mlsanity.com/paper/2405.15813/) (2024)
- [Probing Perceptual Constancy in Large Vision-Language Models](https://mlsanity.com/paper/2502.10273/) (2025)
- [AutoVideo: An Automated Video Action Recognition System](https://mlsanity.com/paper/2108.04212/) (2021)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)

## Abstract (arXiv)

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader.