8 accepted papers
Leveraging the vision foundation models has emerged as a mainstream paradigm that improves the performance of image feature matching. However, previous works have ignored the misalignment when introducing the foundation models into feature matching. The misalignment arises from the discrepancy betwe…
Modeling expert driving behavior is crucial for the successful implementation of human-like autonomous driving. In this paper, we propose a new sampling-based Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework. It leverages naturalistic human driving data to train the reward mode
In the context of surface representations, we find a natural structural similarity between grid surface and image data. Motivated by this inspiration, we propose a novel approach: encoding grid surfaces as geometric images and using image processing methods to address surface optimization-related pr…
Accurate cardiac segmentation in cross-modality images plays an important role in the quantitative analysis of the heart to diagnose cardiovascular diseases. However, achieving high performance in cross-modality segmentation is hindered by the time-consuming annotation and modality gap. While some a…
Achieving fully autonomous driving in urban traffic scenarios is a significant challenge that necessitates balancing safety, efficiency, and compliance with traffic regulations. In this letter, we introduce a novel Curriculum Residual Hierarchical Reinforcement Learning (CR-HRL) framework. It integr
Velocity planning is an important module of autonomous driving, which aims to generate the velocity profile given a reference path. However, most existing algorithms fail to adequately address the uncertainty inherent in driving contexts, leading to potentially risky situations. To this end, we prop
Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore the correlation between them, leading to the lack of consideration for interactio…
By "intelligently" fuse the complementary information across different views, multi-view learning is able to improve the performance of classification task. In this work, we extend the information bottleneck principle to supervised multi-view learning scenario and use the recently proposed matrix-ba…