# Hierarchical Feature Embedding for Attribute Recognition

arXiv: https://arxiv.org/abs/2005.11576 · HTML: https://mlsanity.com/paper/2005.11576/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 16 of 16)

- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [Diversifying Sample Generation for Accurate Data-Free Quantization](https://mlsanity.com/paper/2103.01049/) (2021)
- [Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2109.05686/) (2021)
- [UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval](https://mlsanity.com/paper/2209.02522/) (2022)
- [Learning Single/Multi-Attribute of Object with Symmetry and Group](https://mlsanity.com/paper/2110.04603/) (2021)
- [Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification](https://mlsanity.com/paper/2307.09715/) (2023)
- [Improving Facial Attribute Recognition by Group and Graph Learning](https://mlsanity.com/paper/2105.13825/) (2021)
- [POAR: Towards Open Vocabulary Pedestrian Attribute Recognition](https://mlsanity.com/paper/2303.14643/) (2023)
- [YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition](https://mlsanity.com/paper/2107.06847/) (2021)
- [CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles](https://mlsanity.com/paper/2111.08243/) (2021)
- [GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction](https://mlsanity.com/paper/2203.03079/) (2022)
- [GPTFace: Generative Pre-training of Facial-Linguistic Transformer by Span Masking and Weakly Correlated Text-image Data](https://mlsanity.com/paper/2510.18345/) (2025)
- [MaskMTL: Attribute prediction in masked facial images with deep multitask learning](https://mlsanity.com/paper/2201.03002/) (2022)
- [ASD: Towards Attribute Spatial Decomposition for Prior-Free Facial Attribute Recognition](https://mlsanity.com/paper/2210.13716/) (2022)
- [FAR-AMTN: Attention Multi-Task Network for Face Attribute Recognition](https://mlsanity.com/paper/2601.01537/) (2026)

## Abstract (arXiv)

Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the attribute-level feature embedding, which might perform poorly in complicated heterogeneous conditions. To address this problem, we propose a hierarchical feature embedding (HFE) framework, which learns a fine-grained feature embedding by combining attribute and ID information. In HFE, we maintain the inter-class and intra-class feature embedding simultaneously. Not only samples with the same attribute but also samples with the same ID are gathered more closely, which could restrict the feature embedding of visually hard samples with regard to attributes and improve the robustness to variant conditions. We establish this hierarchical structure by utilizing HFE loss consisted of attribute-level and ID-level constraints. We also introduce an absolute boundary regularization and a dynamic loss weight as supplementary components to help build up the feature embedding. Experiments show that our method achieves the state-of-the-art results on two pedestrian attribute datasets and a facial attribute dataset.