13 accepted papers
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used because of its strong mathematical foundation on safet
With the explosion of human-machine interaction, emotion recognition has reignited attention. Previous works focus on improving visual feature fusion and reasoning from multiple image levels. Although it is non-trivial to deduce a person's emotion by integrating multi-level feature (head, body a…
Analogical reasoning is fundamental to human cognition and plays a crucial role across various fields. However, previous studies have primarily focused on single-modal analogical reasoning, often ignoring the benefits of incorporating structural knowledge. Research in cognitive psychology has shown…
Emotion recognition is an important task in computer vision. However, current approaches using hard associations (e.g., element-wise addition or concatenation) suffer from information pollution. To overcome these challenges and utilize information at different scales, we present a novel Transformer-…
Accurate 3D vascular segmentation is essential for diagnosing and treating vascular diseases. This task remains challenging due to the complexity of the 3D data and the morphological diversity of blood vessels. In recent years, state space models (SSMs) have received a great attention for its good p…
Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remains underexplored. In this paper, we introduce Unilaw-R1, a large language model tailored for legal reasoning. With a lightweight 7-billion parame
Visual bird’s eye view (BEV) perception, dute to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving. However, this type of perception still relies on LiDAR data to construct ground truth databa
As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent universality, they also present challenges in giving ambiguous and uncertain predictions. Significant variations in the model output and granularity can occur with simply subtle changes in the prompt, contradict…
Unsupervised domain adaptation methods have demonstrated superior capabilities in handling the domain shift issue which widely exists in various time series tasks. However, their prominent adaptation performances heavily rely on complex model architectures, posing an unprecedented challenge in deplo…
For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource limited environments (e.g., smartphones). Moreover, due to the inevitable domain shift between model training (source) and deploying (target) stages, compress…
In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker’s emotion. Besides, external commonsense knowledge has been applied to enhance the system’s understandings of the speaker’s situ…
Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the inst…
In this paper, we develop a weakly supervised learning algorithm to learn robust semantic correspondences from large-scale datasets with only image-level labels. Following the spirit of multiple instance learning (MIL), we decompose the weakly supervised correspondence learning problem into three st…