# Single Image Action Recognition using Semantic Body Part Actions

arXiv: https://arxiv.org/abs/1612.04520 · HTML: https://mlsanity.com/paper/1612.04520/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://mlsanity.com/paper/2406.09396/) (2024)
- [HAKE: A Knowledge Engine Foundation for Human Activity Understanding](https://mlsanity.com/paper/2202.06851/) (2022)
- [Understanding Long Videos with Multimodal Language Models](https://mlsanity.com/paper/2403.16998/) (2024)
- [SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization](https://mlsanity.com/paper/2209.02109/) (2022)
- [Can An Image Classifier Suffice For Action Recognition?](https://mlsanity.com/paper/2106.14104/) (2021)
- [GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement](https://mlsanity.com/paper/2208.08965/) (2022)
- [UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing](https://mlsanity.com/paper/2411.16781/) (2024)
- [Grounded Situation Recognition with Transformers](https://mlsanity.com/paper/2111.10135/) (2021)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning](https://mlsanity.com/paper/2112.07015/) (2021)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior](https://mlsanity.com/paper/2105.03089/) (2021)
- [Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning](https://mlsanity.com/paper/2211.06688/) (2022)
- [Image Classifiers are Efficient Self-Supervised Video Representation Learners](https://mlsanity.com/paper/2609.40347/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel single image action recognition algorithm which is based on the idea of semantic body part actions. Unlike existing bottom up methods, we argue that the human action is a combination of meaningful body part actions. In detail, we divide human body into five parts: head, torso, arms, hands and legs. And for each of the body parts, we define several semantic body part actions, e.g., hand holding, hand waving. These semantic body part actions are strongly related to the body actions, e.g., writing, and jogging. Based on the idea, we propose a deep neural network based system: first, body parts are localized by a Semi-FCN network. Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions. And finally, we use SVM to fuse the body part actions and predict the entire body action. Experiments on two dataset: PASCAL VOC 2012 and Stanford-40 report mAP improvement from the state-of-the-art by 3.8% and 2.6% respectively.