# Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification

arXiv: https://arxiv.org/abs/2409.05642 · HTML: https://mlsanity.com/paper/2409.05642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [SPViT: Enabling Faster Vision Transformers via Soft Token Pruning](https://mlsanity.com/paper/2112.13890/) (2021)
- [Cross-Image Relational Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2204.06986/) (2022)
- [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020)
- [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://mlsanity.com/paper/1910.05839/) (2019)
- [CLIP-KD: An Empirical Study of CLIP Model Distillation](https://mlsanity.com/paper/2307.12732/) (2023)
- [Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14481/) (2023)
- [Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2002.04114/) (2020)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [GiT: Graph Interactive Transformer for Vehicle Re-identification](https://mlsanity.com/paper/2107.05475/) (2021)
- [Hierarchical Self-supervised Augmented Knowledge Distillation](https://mlsanity.com/paper/2107.13715/) (2021)
- [Mutual Contrastive Learning for Visual Representation Learning](https://mlsanity.com/paper/2104.12565/) (2021)
- [CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2101.08467/) (2021)
- [Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2304.04205/) (2023)
- [Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition](https://mlsanity.com/paper/2207.11518/) (2022)
- [MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition](https://mlsanity.com/paper/2208.05768/) (2022)
- [Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training](https://mlsanity.com/paper/2211.10801/) (2022)
- [A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models](https://mlsanity.com/paper/2406.07929/) (2024)
- [High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2312.07853/) (2023)
- [COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing](https://mlsanity.com/paper/2402.16424/) (2024)
- [Gated Convolutional Networks with Hybrid Connectivity for Image Classification](https://mlsanity.com/paper/1908.09699/) (2019)
- [Contextuality Helps Representation Learning for Generalized Category Discovery](https://mlsanity.com/paper/2407.19752/) (2024)
- [MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2311.14395/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance](https://mlsanity.com/paper/2503.02760/) (2025)

## Abstract (arXiv)

The primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images, including inter-modal and intra-modal variations. These challenges are further complicated by varying viewpoints and irregular movements. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies and have limited effectiveness in reducing modality discrepancies. In this paper, we propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies by constructing diversified features and mining latent semantically similar features for modal alignment. PDM comprises two key components: Multi-Feature Generation Module (MFGM) and Prototype Learning Module (PLM). The MFGM generates diversity features closely distributed from modality-shared features to represent pedestrians. Additionally, the PLM utilizes learnable prototypes to excavate latent semantic similarities among local features between visible and infrared modalities, thereby facilitating cross-modal instance-level alignment. We introduce the cosine heterogeneity loss to enhance prototype diversity for extracting rich local features. Extensive experiments conducted on the SYSU-MM01 and LLCM datasets demonstrate that our approach achieves state-of-the-art performance. Our codes are available at https://github.com/mmunhappy/ICASSP2025-PDM.