Yuhao Wang

Awesome-Multi-Modal-Object-Re-Identification. Welcome to the Awesome Multi-Modal Object Re-Identification Repository! This repository is dedicated to curating and sharing the latest methods, datasets, and resources focused specifically on the domain of multi-modal object re-identification. It brings together cutting-edge research, tools, and papers aimed at advancing the study and application.

EDITOR. 【CVPR2024】Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification

MambaPro. 【AAAI2025】MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt

DeMo. 【AAAI2025】DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification

TOP-ReID. 【AAAI2024】TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation

IDEA. 【CVPR2025】IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification

FusionReID. 【IEEE TITS2025】Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification

Awesome-Aerial-Ground-Object-Re-Identification. Awesome-AGReID is a curated collection of the latest methods, datasets, and benchmarks for Aerial–Ground Object Re-Identification (AG-ReID). This field focuses on matching objects, mainly people and vehicles, between aerial drone views and ground cameras—an important task for surveillance and security applications.

Awesome-EfficientAI-for-MLLM.

SD-ReID. 【IEEE TIP2026】SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification

VMambaX.

DUTAI. HTML

ERA.

924973292.github.io. JavaScript