The rapid growth of vulnerabilities has significantly accelerated the development of automated vulnerability detection methods, especially those based on data-driven models. However, most of them primarily focus on extracting accurate code representations while overlooking the complex vulnerability patterns among vulnerable statements, thereby leaving room for improvement. To overcome this limitation, we present a novel reinforcement learning framework (RLFD) for detecting vulnerabilities at a fine-grained level.RLFDredefines the detection task as a sequential decision-making process and then employs reinforcement learning to automatically learn vulnerability-relevant structures from code snippets. Moreover, by designing reward functions aligned with fine-grained evaluation metrics,RLFDfocuses on the co-existence relations among statements from a global perspective, enabling the model to capture complex interactions that lead to vulnerabilities. Additionally, the framework utilizes CodeBERT-HLS for code representation, ensuring consistency with the state-of-the-art method while highlighting the improvements brought by the proposed reinforcement learning-based approach. Comprehensive experiments show that our method achieves a locating precision (IoU) of 69.7% and a Top-5% Acc of 67.7% on thebig_vuldataset, outperforming the state-of-the-art method by an overall 3.4% improvement in IoU. Notably, our method achieves up to a 19.7% increase in IoU for specific categories, e.g., CWE-416 (use-after-free).

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a La rge- s cale H igh-diversity [Formula: see text]nchmark for short-term R GBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.