Gen4Track: A Tuning-free Data Augmentation Framework via Self-correcting Diffusion Model for Vision-Language Tracking

The performance of current Vision-Language Tracking (VLT) models is constrained by the limited diversity and quantity of labeled data. Compared to constructing large-scale datasets, data augmentation offers a more cost-saving strategy for VLT by synthesizing new samples from existing data, rather than generating them from scratch. However, conventional techniques like rotation and flipping may disrupt scene composition, causing conflicts between visual layouts and textual annotations. Recent advances in generative models have inspired the use of synthetic videos for data augmentation. Yet, existing approaches fail to address the core concerns of data augmentation in VLT (shown in Fig. 1)-target location accuracy, text-video consistency, and video content coherency. To bridge the gap, we propose Gen4Track, a tuning-free data augmentation framework that leverages the self-correcting mechanism to dynamically generate high-quality video data with annotations. Our approach involves (1) optimizing the attention calculations in a frozen text-to-image diffusion model to synthesize coherent videos that satisfy specific conditions (e.g., spatial location, category, color, and style), and (2) implementing a self-correcting mechanism based on a Large Language Model (LLM) to improve text-video consistency. During video augmentation, we propose content-coherent self-attention and location-enhanced cross-attention mechanisms, ensuring that image-level editings are accurately and coherently propagated throughout the video. Then, with the goal of maximizing text-video consistency, we iteratively refine the augmentation instruction with our designed self-correcting mechanism for a more aligned video. Extensive experiments validate that Gen4Track significantly boosts the performance of SOTA VLT models (achieving improvements of up to 3.2% in SUC and 3.5% in PRE), opening a new chapter of training Vision-Language trackers with synthetic videos rather than manually annotated data.