The paper introduces DTLLM-VLT, a model that generates diverse and multi-granularity text descriptions for visual language tracking (VLT) datasets, enhancing the performance of object tracking systems. VLT integrates natural language descriptions from videos to improve the precision of single object tracking (SOT). However, existing VLT benchmarks often lack a coherent semantic framework and rely on single-granularity annotations, which can be labor-intensive and time-consuming to create. DTLLM-VLT addresses these challenges by automatically generating scientific and multi-granularity text descriptions using a cohesive prompt framework. The model is designed to seamlessly integrate into various VLT benchmarks, providing four levels of granularity: initial concise, initial detailed, dense concise, and dense detailed. The authors evaluate DTLLM-VLT on three prominent VLT benchmarks—OTB99 Lang, LaSOT, and MGIT—showing that the diverse text descriptions significantly improve tracking performance. The results highlight the benefits of a diversified environment and suggest potential enhancements to multi-modal learning capabilities through generated text data. The paper concludes by discussing the contributions of DTLLM-VLT and its potential for further research in vision datasets understanding.The paper introduces DTLLM-VLT, a model that generates diverse and multi-granularity text descriptions for visual language tracking (VLT) datasets, enhancing the performance of object tracking systems. VLT integrates natural language descriptions from videos to improve the precision of single object tracking (SOT). However, existing VLT benchmarks often lack a coherent semantic framework and rely on single-granularity annotations, which can be labor-intensive and time-consuming to create. DTLLM-VLT addresses these challenges by automatically generating scientific and multi-granularity text descriptions using a cohesive prompt framework. The model is designed to seamlessly integrate into various VLT benchmarks, providing four levels of granularity: initial concise, initial detailed, dense concise, and dense detailed. The authors evaluate DTLLM-VLT on three prominent VLT benchmarks—OTB99 Lang, LaSOT, and MGIT—showing that the diverse text descriptions significantly improve tracking performance. The results highlight the benefits of a diversified environment and suggest potential enhancements to multi-modal learning capabilities through generated text data. The paper concludes by discussing the contributions of DTLLM-VLT and its potential for further research in vision datasets understanding.