# TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification

arXiv: https://arxiv.org/abs/2503.00477 · HTML: https://mlsanity.com/paper/2503.00477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [Clothes-Changing Person Re-identification with RGB Modality Only](https://mlsanity.com/paper/2204.06890/) (2022)
- [Person Re-identification by Contour Sketch under Moderate Clothing Change](https://mlsanity.com/paper/2002.02295/) (2020)
- [Long-Term Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2005.12633/) (2020)
- [Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization](https://mlsanity.com/paper/2103.15537/) (2021)
- [When Person Re-identification Meets Changing Clothes](https://mlsanity.com/paper/2003.04070/) (2020)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [Lightweight Multi-Branch Network for Person Re-Identification](https://mlsanity.com/paper/2101.10774/) (2021)
- [Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification](https://mlsanity.com/paper/2010.05435/) (2020)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2108.04527/) (2021)
- [Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2308.10692/) (2023)
- [How important are faces for person re-identification?](https://mlsanity.com/paper/2010.06307/) (2020)
- [RealGait: Gait Recognition for Person Re-Identification](https://mlsanity.com/paper/2201.04806/) (2022)
- [Sampling Agnostic Feature Representation for Long-Term Person Re-identification](https://mlsanity.com/paper/2209.09574/) (2022)
- [Causal Clothes-Invariant Feature Learning for Cloth-Changing Person Re-ID](https://mlsanity.com/paper/2305.06145/) (2023)
- [Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2208.12023/) (2022)
- [Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences](https://mlsanity.com/paper/2310.18438/) (2023)
- [Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2405.16597/) (2024)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)
- [Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2411.00330/) (2024)
- [Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2410.03977/) (2024)

## Abstract (arXiv)

Cloth-Changing Person Re-identification (CC-ReID) aims to solve the challenge of identifying individuals across different temporal-spatial scenarios, viewpoints, and clothing variations. This field is gaining increasing attention in big data research and public security domains. Existing ReID research primarily relies on face recognition, gait semantic recognition, and clothing-irrelevant feature identification, which perform relatively well in scenarios with high-quality clothing change videos and images. However, these approaches depend on either single features or simple combinations of multiple features, making further performance improvements difficult. Additionally, limitations such as missing facial information, challenges in gait extraction, and inconsistent camera parameters restrict the broader application of CC-ReID. To address the above limitations, we innovatively propose a Tri-Stream Dynamic Weight Network (TSDW) that requires only images. This dynamic weighting network consists of three parallel feature streams: facial features, head-limb features, and global features. Each stream specializes in extracting its designated features, after which a gating network dynamically fuses confidence levels. The three parallel feature streams enhance recognition performance and reduce the impact of any single feature failure, thereby improving model robustness. Extensive experiments on benchmark datasets (e.g., PRCC, Celeb-reID, VC-Clothes) demonstrate that our method significantly outperforms existing state-of-the-art approaches.