# Attribute Aware Pooling for Pedestrian Attribute Recognition

arXiv: https://arxiv.org/abs/1907.11837 · HTML: https://mlsanity.com/paper/1907.11837/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting](https://mlsanity.com/paper/2107.03576/) (2021)
- [Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion](https://mlsanity.com/paper/2312.10692/) (2023)
- [PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2304.07230/) (2023)
- [SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm](https://mlsanity.com/paper/2312.01640/) (2023)
- [An Empirical Study of Mamba-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2407.10374/) (2024)
- [Robust Pedestrian Attribute Recognition Using Group Sparsity for Occlusion Videos](https://mlsanity.com/paper/2110.08708/) (2021)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [Learning Disentangled Label Representations for Multi-label Classification](https://mlsanity.com/paper/2212.01461/) (2022)
- [Attribute Guided Sparse Tensor-Based Model for Person Re-Identification](https://mlsanity.com/paper/2108.04352/) (2021)

## Abstract (arXiv)

This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition.