How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking

TL;DR

VLTVerse is proposed, the first fine-grained evaluation framework for VLT trackers that comprehensively considers multiple challenge factors and diverse semantic information, hoping to reveal the role of language in VLT.

Abstract

Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under challenging conditions like fast motion and deformations. However, current VLT trackers often underperform compared to single-modality methods on multiple benchmarks, with semantic information sometimes becoming a "distraction." To address this, we propose VLTVerse, the first fine-grained evaluation framework for VLT trackers that comprehensively considers multiple challenge factors and diverse semantic information, hoping to reveal the role of language in VLT. Our contributions include: (1) VLTVerse introduces 10 sequence-level challenge labels and 6 types of multi-granularity semantic information, creating a flexible and multi-dimensional evaluation space for VLT; (2) leveraging 60 subspaces formed by combinations of challenge factors and semantic types, we conduct systematic fine-grained evaluations of three mainstream SOTA VLT trackers, uncovering their performance bottlenecks across complex scenarios and offering a novel perspective on VLT evaluation; (3) through decoupled analysis of experimental results, we examine the impact of various semantic types on specific challenge factors in relation to different algorithms, providing essential guidance for enhancing VLT across data, evaluation, and algorithmic dimensions. The VLTVerse, toolkit, and results will be available at \url{http://metaverse.aitestunion.com}.

Figures (7)

• Figure 1: Examples of tracking results by JointNLT jointnlt under sequences with three different challenge factors combined with various texts. We select representative sequences under the three most challenging factors (delta ratio, fast motion, and correlation coefficient) from two tracking datasets (LaSOT lasot and TNL2K tnl2k). It is evident that the tracking performance of JointNLT varies significantly with different textual assistance, and the figure labels the text that results in the best tracking performance. Faced with different challenge factors, different semantic information might be needed to provide guidance. Otherwise, the text could become a distraction. VLTVerse reveals the shortcomings of traditional evaluation methods and offers guidance for tracker optimization from the perspective of fine-grained evaluation.
• Figure 2: VLTVerse comprises two main components: environment and evaluation. As an extension of SOTVerse sotverse, it expands the evaluation space into three dimensions—normal space, challenge factor space, and textual information space. The normal space covers short-term, long-term, and global instance tracking tasks. The challenge factor space is defined by 10 attributes corresponding to 10 distinct challenge factors, while the textual information space includes 6 types of semantic descriptions. This three-dimensional framework enables a comprehensive evaluation of tracking performance under various language and challenge conditions. Using the OPE evaluation system, we assess tracker performance across different challenge factor spaces with diverse textual inputs. Key evaluation metrics include SR, SUC, PRE, N-PRE, and AUC. Based on the defined environment and evaluation setup, researchers can design customized executors by combining specific textual information and challenge factors, thus creating experimental settings that allow for a fine-grained analysis of language's role in VLT.
• Figure 3: Left: Example of challenging factors, with four static challenging factors and six dynamic challenging factors. Right: Example of textual information, providing six types of information for each video sequence, including Attribute Words, Dense Concise, Dense Detailed, Initial Concise, Initial Detailed, and Blank information.
• Figure 4: Radar chart of the Average Value (AV) and Coefficient of Variation (CV) of tracking performance under different textual information guidance for various challenging factors (based on SUC).
• Figure 5: Optimal semantic information for tracking performance under various challenge factors. We use a star, diamond, and square to indicate the best textual information for MMTrack mmtrack, JointNLT jointnlt, and UVLTrack uvltrack, respectively.