Zhangyong-Tang/Awesome-MultiModal-Visual-Object-Tracking

A continuously updated project to track the latest progress in the field of multi-modal object tracking. This project focuses solely on single-object tracking.

[ACM MM 2026 Oral] AnyTrack: Unifying Visual Object Tracking with Any Modalities

Resources for Multiple Object Tracking (MOT)

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.

A collection of deep learning based RGB-T-Fusion methods, codes, and datasets. The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking.

RGB-T Fusion, RGB-T SOD, RGB-T Vehicle Detection, RGB-T Crowd Counting, RGB-T Pedestrian Detection, RGB-T Semantic Segmeantaion, RGB-T Tracking

[IJCV 2026] Multimodal Referring Segmentation

This repository is a paper digest of Transformer-related approaches in visual tracking tasks.