Self-Chained Dynamic Context Perception to Tracking by Natural Language Specification

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Vision-language cross-modal learning significantly advances Tracking by Natural Language specification (TNL).
• Existing TNL methods often struggle to model dynamic target-state changes described in natural language queries.
• Pre-trained representations lack explicit focus on action-related linguistic cues, limiting performance.

Purpose of the Study:

• To propose Self-Chained Dynamic Context Perception (SeDCP), a novel framework for TNL.
• To explicitly model dynamic linguistic cues and enhance language-guided visual refinement.
• To improve the accuracy and robustness of tracking objects based on natural language specifications.

Main Methods:

• Developed a self-chained framework (SeDCP) with coupled Forward and Backward Chains.
• Forward Chain modulates dynamic queries using trajectory-aware spatiotemporal cues.
• Backward Chain refines visual features using dynamically enhanced query representations.
• Introduced sequence-level matching and a Global-Local enhanced video Transformer.

Main Results:

• SeDCP consistently outperforms state-of-the-art methods across seven standard TNL benchmarks.
• Demonstrated strong generalization capabilities on the unseen LaSOT_ext benchmark.
• Validated the effectiveness of explicit dynamic query modulation and language-guided visual refinement.

Conclusions:

• SeDCP effectively addresses the limitations of existing TNL methods in modeling dynamic linguistic cues.
• The proposed framework enhances cross-modal alignment for improved object tracking accuracy.
• SeDCP shows significant potential for real-world applications requiring precise natural language-guided tracking.