Vision-Language Models (VLMs), exemplified by CLIP, have emerged as foundational for multimodal intelligence. However, their capacity for logical understanding remains significantly underexplored, resulting in critical **''logical blindspots''** that limit their reliability in practical applications. To systematically diagnose this, we introduce **LogicBench**, a comprehensive benchmark with over 50,000 vision-language pairs across 9 logical categories and 4 diverse scenarios: images, videos, anomaly detection, and medical diagnostics. Our evaluation reveals that existing VLMs, even the state-of-the-art ones, fall at over 40 accuracy points below human performance, particularly in challenging tasks like Causality and Conditionality, highlighting their reliance on surface semantics over critical logical structures. To bridge this gap, we propose **LogicCLIP**, a novel training framework designed to boost VLMs' logical sensitivity through advancements in both data generation and optimization objectives. LogicCLIP utilizes logic-aware data generation and a contrastive learning strategy that combines coarse-grained alignment, a fine-grained multiple-choice objective, and a novel logical structure-aware objective. Extensive experiments demonstrate LogicCLIP's substantial improvements in logical comprehension across all LogicBench domains, significantly outperforming baselines. Moreover, LogicCLIP retains, and often surpasses, competitive performance on general vision-language benchmarks, demonstrating that the enhanced logical understanding does not come at the expense of general alignment. We believe LogicBench and LogicCLIP will be important resources for advancing VLM logical capabilities.

Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to capture the cognitive motivations behind attention allocation in specific contexts, which limits deeper understanding of attention mechanisms. To bridge this gap, we introduce Explainable Driver Attention Prediction, a novel task paradigm that jointly predicts spatial attention regions (where), parses attended semantics (what), and provides cognitive reasoning for attention allocation (why). To support this, we present W3DA, the first large-scale explainable driver attention dataset. It enriches existing benchmarks with detailed semantic and causal annotations across diverse driving scenarios, including normal conditions, safety-critical situations, and traffic accidents. We further propose LLada, a Large Language model-driven framework for driver attention prediction, which unifies pixel modeling, semantic parsing, and cognitive reasoning within an end-to-end architecture. Extensive experiments demonstrate the effectiveness of LLada, exhibiting robust generalization across datasets and driving conditions. This work serves as a key step toward a deeper understanding of driver attention mechanisms, with significant implications for autonomous driving, intelligent driver training, and human-computer interaction.

Despite significant advancements in road event detection (RED), existing approaches encounter critical limitations. These include reliance on single-modal inputs and joint optimization of detection and classification tasks, often leading to conflicting objectives and suboptimal performance. Moreover, their heavy dependence on large-scale annotated datasets restricts generalization in data-scarce scenarios. To address these challenges, we propose AdaRED, a novel framework that decouples agent detection from event classification, thereby mitigating optimization conflicts and enhancing task-specific performance. To comprehensively understand road events, AdaRED uses diverse input modalities, including fine-grained local features, global contextual information, and spatial layout embeddings. Additionally, we introduce the Cross-modal Scene Adaptation Module (CSAM), which integrates lightweight adapters into the multimodal model. This design enables efficient extraction of spatiotemporal features and the integration of visual priors, thereby improving generalization and robustness in challenging scenarios. Extensive experiments on the ROAD-R dataset validate the effectiveness of AdaRED, achieving state-of-the-art performance and addressing the limitations of existing methods. The code can be found on our project page: https://liulinkai.github.io/AdaRED/.

Magnetic resonance imaging (MRI) vertebral localization, identification, and segmentation are important steps in the automatic analysis of spines. Due to the similar appearances of vertebrae, the accurate segmentation, localization, and identification of vertebrae remain challenging. Previous methods solved the three tasks independently, ignoring the intrinsic correlation among them. In this paper, we propose a multi-task relational learning network (MRLN) that utilizes both the relationships between vertebrae and the relevance of the three tasks. A dilation convolution group is used to expand the receptive field, and LSTM(Long Short-Term Memory) to learn the prior knowledge of the order relationship between the vertebral bodies. We introduce a co-attention module to learn the correlation information, localization-guided segmentation attention(LGSA) and segmentation-guided localization attention(SGLA), in the decoder stage of segmentation and localization tasks. Learning two tasks simultaneously as well as the correlation between tasks can not only avoid the overfitting of a single task but also correct each other. To avoids the cumbersome weight adjustment for different tasks loss functions, we formulated a novel XOR loss that provides a direct evaluation criterion for the localization relationship of the semantic location regression and semantic segmentation. This method was evaluated on a dataset which includes multiple MRI modalities (T1 and T2), various fields of view. Experimental results demonstrate that both of the co-attention and XOR loss work outperforms the most recent state of art.