10 accepted papers
Collaborative perception enables Connected Autonomous Vehicles (CAVs) to share sensory data, and therefore presents a promising path towards long-range robust environmental understanding by overcoming individual perception limitations such as occlusions. The core challenge of collaborative perceptio
Visual Bird's-Eye View (BEV) perception is a foundational paradigm in autonomous driving, enabling top-down semantic-spatial representations from multi-view inputs. However, current BEV methods struggle with occlusions, often generating hallucinated predictions in regions that are unobserved by the
Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: th
Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, current approaches sharing dense Bird
Surveys are widely used to collect patient data in healthcare, and there is significant clinical interest in predicting patient outcomes using survey data. However, surveys often include numerous features that lead to high-dimensional inputs for machine learning models. This paper exploits a unique…
This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs). Existing works have proposed token reduction approaches to improve the efficiency of ViT-based image c…
Semi-Supervised Video Salient Object Detection (SS-VSOD) is challenging because of the lack of temporal information in video sequences caused by sparse annotations. Most works address this problem by generating pseudo labels for unlabeled data. However, error-prone pseudo labels negatively affect th…
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly u…
Semantic scene understanding is an essential task for self-driving vehicles and mobile robots. In our work, we aim to estimate a semantic segmentation map, in which the foreground objects are removed and semantically inpainted with background classes, from a single RGB image. This semantic foregroun
In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view mapping. At the core, it utilizes a variational encoder–decoder networ