# Cerberus: Attribute-based person re-identification using semantic IDs

arXiv: https://arxiv.org/abs/2412.01048 · HTML: https://mlsanity.com/paper/2412.01048/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark](https://mlsanity.com/paper/1804.01984/) (2018)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2107.02380/) (2021)
- [Attribute-Aware Attention Model for Fine-grained Representation Learning](https://mlsanity.com/paper/1901.00392/) (2019)
- [Deep Attention Aware Feature Learning for Person Re-Identification](https://mlsanity.com/paper/2003.00517/) (2020)
- [Attributes-aided Part Detection and Refinement for Person Re-identification](https://mlsanity.com/paper/1902.10528/) (2019)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)

## Built on by (most cited first, 2 of 2)

- [DiCo: Disentangled Concept Representation for Text-to-image Person Re-identification](https://mlsanity.com/paper/2601.10053/) (2026)
- [Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID](https://mlsanity.com/paper/2604.15090/) (2026)

## Abstract (arXiv)

We introduce a new framework, dubbed Cerberus, for attribute-based person re-identification (reID). Our approach leverages person attribute labels to learn local and global person representations that encode specific traits, such as gender and clothing style. To achieve this, we define semantic IDs (SIDs) by combining attribute labels, and use a semantic guidance loss to align the person representations with the prototypical features of corresponding SIDs, encouraging the representations to encode the relevant semantics. Simultaneously, we enforce the representations of the same person to be embedded closely, enabling recognizing subtle differences in appearance to discriminate persons sharing the same attribute labels. To increase the generalization ability on unseen data, we also propose a regularization method that takes advantage of the relationships between SID prototypes. Our framework performs individual comparisons of local and global person representations between query and gallery images for attribute-based reID. By exploiting the SID prototypes aligned with the corresponding representations, it can also perform person attribute recognition (PAR) and attribute-based person search (APS) without bells and whistles. Experimental results on standard benchmarks on attribute-based person reID, Market-1501 and DukeMTMC, demonstrate the superiority of our model compared to the state of the art.