6 accepted papers
Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. However, troublesome gaps remains between current VLMs and real-world autonomous driving applications. One major limitation is that existing datasets with loosely…
Pedestrian trajectory prediction has always been a key issue in engineering fields such as autonomous driving. Diffusion model can be used to solve the problem of multimodal pedestrian trajectory generation, but the excessive denoising steps in the model result in slow inference speed for this type
The realization of autonomous driving(AV) is closely linked to the development of intelligent decision-making modules that can operate safely in dynamic, uncertain environments. To address issues such as delayed response and poor coupling in highway scenarios, this paper proposes a hierarchical Coup
RGB-T object detection in autonomous driving has been researched increasingly in recent years. Nevertheless, several problems limit the performance of RGB-T fusion perception. Initially, although illumination awareness is a mature technology to guide fusion process, the outputs of previous methods l
Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has made great achievements in medical image segment…
Hypertension is one of the most important contributors to heart disease and stroke. Multimodality medical image fusion plays an important role in the precise diagnosis, treatment planning and follow-up studies of various diseases. In this paper, we propose an image fusion framework in patients with…