ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking

TL;DR

ATCTrack introduces a novel multimodal tracking approach that aligns dynamic target states with visual and textual cues, significantly improving robustness in complex long-term vision-language tracking scenarios.

Contribution

The paper proposes a new tracker that models target-context features for better alignment with dynamic target states, incorporating temporal visual modeling and textual context calibration.

Findings

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

Effective temporal visual target-context modeling enhances tracking robustness.

Textual target words identification improves cue utilization.

Abstract

Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame. To achieve robust tracking, especially in complex long-term scenarios that reflect real-world conditions as recently highlighted by MGIT, it is essential not only to characterize the target features but also to utilize the context features related to the target. However, the visual and textual target-context cues derived from the initial prompts generally align only with the initial target state. Due to their dynamic nature, target states are constantly changing, particularly in complex long-term sequences. It is intractable for these cues to continuously guide Vision-Language Trackers (VLTs). Furthermore, for the text prompts with diverse expressions, our experiments reveal that existing VLTs struggle to discern which words pertain to the target or the context, complicating the utilization of textual cues. In this work, we present a novel tracker named ATCTrack, which can obtain multimodal cues Aligned with the dynamic target states through comprehensive Target-Context feature modeling, thereby achieving robust tracking. Specifically, (1) for the visual modality, we propose an effective temporal visual target-context modeling approach that provides the tracker with timely visual cues. (2) For the textual modality, we achieve precise target words identification solely based on textual content, and design an innovative context words calibration method to adaptively utilize auxiliary context words. (3) We conduct extensive experiments on mainstream benchmarks and ATCTrack achieves a new SOTA performance. The code and models will be released at: https://github.com/XiaokunFeng/ATCTrack.