9 accepted papers
Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scenes reconstruction and novel view synthesis. However, most existing methods either rely solely on cameras or use LiDAR only for Gaussian initialization or depth supervision, while the rich scene informat…
Ensuring accessible pedestrian navigation requires reasoning about both semantic and spatial aspects of complex urban scenes, a challenge that existing Large Vision-Language Models (LVLMs) struggle to meet. Although these models can describe visual content, their lack of explicit grounding leads to
Visual SLAM has regained attention due to its ability to provide perceptual capabilities and simulation test data for Embodied AI. However, traditional SLAM methods struggle to meet the demands of high-quality scene reconstruction, and Gaussian SLAM systems, despite their rapid rendering and high-qu
Bird's eye view (BEV) segmentation map is a recent development in autonomous driving that provides effective environmental information, such as drivable areas and lane dividers. Most of the existing methods use cameras and LiDAR as inputs for segmentation and the fusion of different modalities is ac
To avoid unmanned ground vehicles being obstructed by deformed terrain in off-road, effective vehicle mobility analysis is required. However, the computational complexity of existing mobility analysis methods, such as discrete element analysis, poses significant challenges when applied to large-scal
Semi-supervised object detection (SSOD) is a significant application of Semi-supervised learning to further improve object detectors but suffers more seriously from confirmation bias and error accumulation caused by the classes imbalance. Existing SSOD approaches have attempted to address this issue…
Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped information to still flow through the networks. Some structured forms of dropout have been proposed to address this but are…
Automatic speech recognition systems suffer from accuracy degradation when code-switching (multiple languages are spoken in a single utterance) is encountered. This is especially common for non-native speakers where there is a mismatch between speech and acoustic model. In this paper, we experiment…