# Actions and Attributes from Wholes and Parts

arXiv: https://arxiv.org/abs/1412.2604 · HTML: https://mlsanity.com/paper/1412.2604/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2014.

## Built on by (most cited first, 12 of 12)

- [Mining Cross-Person Cues for Body-Part Interactiveness Learning in HOI Detection](https://mlsanity.com/paper/2207.14192/) (2022)
- [HAKE: A Knowledge Engine Foundation for Human Activity Understanding](https://mlsanity.com/paper/2202.06851/) (2022)
- [Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos](https://mlsanity.com/paper/2207.09425/) (2022)
- [Video Action Recognition with Attentive Semantic Units](https://mlsanity.com/paper/2303.09756/) (2023)
- [Part-Based Models Improve Adversarial Robustness](https://mlsanity.com/paper/2209.09117/) (2022)
- [HASSOD: Hierarchical Adaptive Self-Supervised Object Detection](https://mlsanity.com/paper/2402.03311/) (2024)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2407.00917/) (2024)
- [Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior](https://mlsanity.com/paper/2105.03089/) (2021)
- [Human Action Recognition in Still Images Using ConViT](https://mlsanity.com/paper/2307.08994/) (2023)
- [Transferable Interactiveness Knowledge for Human-Object Interaction Detection](https://mlsanity.com/paper/2101.10292/) (2021)
- [Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts](https://mlsanity.com/paper/2508.06032/) (2025)

## Abstract (arXiv)

We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. Our part detectors are a deep version of poselets and capture parts of the human body under a distinct set of poses. For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. In addition, we demonstrate the effectiveness of our approach when we replace an oracle person detector, as is the default in the current evaluation protocol for both tasks, with a state-of-the-art person detection system.