# Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

arXiv: https://arxiv.org/abs/2607.14821 · HTML: https://mlsanity.com/paper/2607.14821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 242 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search](https://mlsanity.com/paper/2101.03036/) (2021)
- [Text-Based Person Search with Limited Data](https://mlsanity.com/paper/2110.10807/) (2021)
- [Person Re-identification Meets Image Search](https://mlsanity.com/paper/1502.02171/) (2015)
- [Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification](https://mlsanity.com/paper/1905.10048/) (2019)
- [UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification](https://mlsanity.com/paper/2310.18812/) (2023)
- [CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2110.08994/) (2021)
- [RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification](https://mlsanity.com/paper/2411.01225/) (2024)
- [A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification](https://mlsanity.com/paper/2007.01504/) (2020)
- [PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification](https://mlsanity.com/paper/2408.16684/) (2024)
- [Dynamic Enhancement Network for Partial Multi-modality Person Re-identification](https://mlsanity.com/paper/2305.15762/) (2023)
- [ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model](https://mlsanity.com/paper/2506.09385/) (2025)
- [Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification](https://mlsanity.com/paper/2003.00213/) (2020)
- [Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search](https://mlsanity.com/paper/2303.04497/) (2023)
- [Appearance Descriptors for Person Re-identification: a Comprehensive Review](https://mlsanity.com/paper/1307.5748/) (2013)
- [Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification](https://mlsanity.com/paper/2304.09498/) (2023)
- [Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment](https://mlsanity.com/paper/2404.06683/) (2024)
- [Prompt Decoupling for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2401.02173/) (2024)

## Abstract (arXiv)

Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.