Wei Zhang

Ruitong Li, Lin Zhang, Yuenan Zhao et al.

Deep reinforcement learning (DRL) methods have demonstrated potential for autonomous navigation and obstacle avoidance of unmanned ground vehicles (UGVs) in crowded environments. Most existing approaches rely on single-frame observation and employ simple concatenation for multi-modal fusion, which limits their ability to capture temporal context and hinders dynamic adaptability. To address these challenges, we propose a DRL-based navigation framework, DRL-TH, which leverages temporal graph attention and hierarchical graph pooling to integrate historical observations and adaptively fuse multi-modal information. Specifically, we introduce a temporal-guided graph attention network (TG-GAT) that incorporates temporal weights into attention scores to capture correlations between consecutive frames, thereby enabling the implicit estimation of scene evolution. In addition, we design a graph hierarchical abstraction module (GHAM) that applies hierarchical pooling and learnable weighted fusion to dynamically integrate RGB and LiDAR features, achieving balanced representation across multiple scales. Extensive experiments demonstrate that our DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios.

Wei Zhang, Chaoqun Wang, Zixuan Guan et al.

Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicability to multi-instance tasks such as detection, segmentation, and pose estimation. In real-world scenarios, error slices often arise from corner cases involving complex visual relationships, where existing instance-level approaches lacking fine-grained reasoning struggle to yield meaningful insights. Moreover, current benchmarks are typically tailored to specific algorithms or biased toward image classification, with artificial ground truth that fails to reflect real model failures. To address these limitations, we propose SliceLens, a hypothesis-driven framework that leverages LLMs and VLMs to generate and verify diverse failure hypotheses through grounded visual reasoning, enabling reliable identification of fine-grained and interpretable error slices. We further introduce FeSD (Fine-grained Slice Discovery), the first benchmark specifically designed for evaluating fine-grained error slice discovery across instance-level vision tasks, featuring expert-annotated and carefully refined ground-truth slices with precise grounding to local error regions. Extensive experiments on both existing benchmarks and FeSD demonstrate that SliceLens achieves state-of-the-art performance, improving Precision@10 by 0.42 (0.73 vs. 0.31) on FeSD, and identifies interpretable slices that facilitate actionable model improvements, as validated through model repair experiments.