Towards Universal Modal Tracking with Online Dense Temporal Token Learning

TL;DR

This paper introduces {oldsymbol{ exttt{modaltracker}}}, a universal, online dense temporal token learning model for multi-modal video tracking that supports various modalities and achieves state-of-the-art results.

Contribution

The paper presents a unified model architecture with online dense temporal token association and modality-scalable gated perceivers for multi-modal video tracking.

Findings

Achieves new state-of-the-art performance on multiple benchmarks.

Supports various modalities with a single model architecture.

Reduces training complexity through one-shot training scheme.

Abstract

We propose a universal video-level modality-awareness tracking model with online dense temporal token learning (called {\modaltracker}). It is designed to support various tracking tasks, including RGB, RGB+Thermal, RGB+Depth, and RGB+Event, utilizing the same model architecture and parameters. Specifically, our model is designed with three core goals: \textbf{Video-level Sampling}. We expand the model's inputs to a video sequence level, aiming to see a richer video context from an near-global perspective. \textbf{Video-level Association}. Furthermore, we introduce two simple yet effective online dense temporal token association mechanisms to propagate the appearance and motion trajectory information of target via a video stream manner. \textbf{Modality Scalable}. We propose two novel gated perceivers that adaptively learn cross-modal representations via a gated attention mechanism, and subsequently compress them into the same set of model parameters via a one-shot training manner for multi-task inference. This new solution brings the following benefits: (i) The purified token sequences can serve as temporal prompts for the inference in the next video frames, whereby previous information is leveraged to guide future inference. (ii) Unlike multi-modal trackers that require independent training, our one-shot training scheme not only alleviates the training burden, but also improves model representation. Extensive experiments on visible and multi-modal benchmarks show that our {\modaltracker} achieves a new \textit{SOTA} performance. The code will be available at https://github.com/GXNU-ZhongLab/ODTrack.