CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

2026-04-29 · Fengchun Zhang, Qiang Ma, Liuyu Xiang, Jinshan Lai, Tingxuan Huang, Jianwei Hu arxiv

Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO-EVO, a novel federated framework that resolves this semantic-style conflict through a co-evolutionary mechanism. On the semantic side, Camera-Invariant Semantic Anchoring (CSA) learns identity prompts with cross-camera consistency to establish purified and domain-agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera-Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO-EVO is its co-evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO-EVO achieves state-of-the-art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross-domain generalization. Our code is available at: https://github.com/NanYiyuzurn/ACL-LGPS-2026.

📄 PDF Abstract BibTeX arXiv:2604.26363

Code (0)

등록된 구현이 없습니다.

Tasks

Person Re-Identification Domain Generalization

Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

2026-04-24 · Hefeng Zhou, Xuan Liu, Sicheng Chen, Wutong Zhang 외 arxiv

Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modalities. Under such heterogeneity, a single global model is often insuffi…

Cross-Modal Retrieval

SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring

2026-06-28 · Yufei Zhang, Chenlu Zhan, Gaoang Wang, Hongwei Wang arxiv

Open-vocabulary 3D semantic Gaussian field learning relies on multi-view 2D supervision, whose semantic targets and spatial assignments are often unreliable. Across varying viewpoints, view-dependent features cause seman…

Semantic Segmentation

MAST: Mask-Guided Attention Mass Allocation for Training-Free Multi-Style Transfer

2026-04-14 · Dongkyung Kang, Jaeyeon Hwang, Junseo Park, Minji Kang 외 arxiv

Style transfer aims to render a content image with the visual characteristics of a reference style while preserving its underlying semantic layout and structural geometry. While recent diffusion-based models demonstrate …

Style Transfer

Feature Diversification and Adaptation for Federated Domain Generalization

2024-07-11 · Seunghan Yang, Seokeon Choi, Hyunsin Park, Sungha Choi 외

Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their limited domains, leading to a `domain sh…

Domain Generalization Federated Learning

Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition

2025-09-19 · Shreya G. Upadhyay, Carlos Busso, Chi-Chun Lee arxiv

Cross-lingual speech emotion recognition (SER) remains a challenging task due to differences in phonetic variability and speaker-specific expressive styles across languages. Effectively capturing emotion under such diver…

Speech Emotion Recognition