# Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning

arXiv: https://arxiv.org/abs/2606.12258 · HTML: https://mlsanity.com/paper/2606.12258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks](https://mlsanity.com/paper/2406.08394/) (2024)
- [LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning](https://mlsanity.com/paper/2306.01293/) (2023)
- [Consistency-guided Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2306.01195/) (2023)
- [Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2212.00226/) (2022)
- [Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.06825/) (2024)
- [Unsupervised Hyperspectral Image Super-Resolution via Self-Supervised Modality Decoupling](https://mlsanity.com/paper/2412.04802/) (2024)
- [Region Generation and Assessment Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2309.03558/) (2023)
- [Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification](https://mlsanity.com/paper/2312.16797/) (2023)
- [CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.05806/) (2024)
- [Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID](https://mlsanity.com/paper/2305.12673/) (2023)
- [Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2402.19026/) (2024)
- [Robust Pseudo-label Learning with Neighbor Relation for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2405.05613/) (2024)
- [Relieving Universal Label Noise for Unsupervised Visible-Infrared Person Re-Identification by Inferring from Neighbors](https://mlsanity.com/paper/2412.12220/) (2024)
- [Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2603.05235/) (2026)
- [Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning](https://mlsanity.com/paper/2312.16246/) (2023)
- [Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment](https://mlsanity.com/paper/2404.06683/) (2024)

## Abstract (arXiv)

Cross-domain day-night re-identification (ReID) is fundamentally challenged by the substantial visual appearance discrepancies between daytime and nighttime scenes. Existing fully supervised methods rely heavily on labor-intensive annotations, which are costly and exhibit limited generalization across domains. In this work, we investigate unsupervised day-night ReID and propose a novel framework that synergistically combines prompt learning and prototype-based representation learning to associate identities across domains without requiring manual labels. Our approach follows a progressive two-stage training strategy. In the first stage, we exploit the vision-language model to generate instance-specific textual prompts in an annotation-free manner. We employ an instance-level alignment mechanism to embed visual features and textual prompts into a unified semantic space, aligning unlabeled day/night images with learnable prompts via instance-aware dynamic-bias adaptation. In the second stage, we construct domain-specific prototype memory banks and introduce two complementary modules: i) an intra-domain identity association module to enhance feature discriminability within each domain, and ii) a cross-domain prototype matching module to reliably identify positive and negative prototype pairs, thereby establishing robust identity correspondences across day and night. Extensive experiments on public benchmarks validate the effectiveness of our method. Under the unsupervised setting, our framework attains Rank-1 accuracy comparable to state-of-the-art fully supervised methods.