The article presents a novel method for automatic semantic segmentation of MRI images using unsupervised federated domain adaptation. This approach is designed to overcome the challenges posed by the variability…
Continue readingThis research paper presents a language-assisted approach to point cloud feature learning (LAST-PCL), aimed at improving 3D scene understanding. The approach incorporates knowledge from other modalities, like images and text,…
Continue readingThe paper tackles the challenge of generalization in neural networks to new domains not seen during training. The authors introduce a simple framework for generalizing semantic segmentation networks by using…
Continue readingThe article by Francesco Croce and Matthias Hein from University of Tübingen AI Center proposes a new approach to adversarial attacks on image segmentation models. These models typically generate segmentation…
Continue readingMobile-Seed is a lightweight, dual-task framework designed for simultaneous semantic segmentation and boundary detection. It’s essential for numerous robotic tasks such as robot grasping, real-time semantic mapping, and sensor calibration….
Continue readingThis article discusses a new approach to simultaneously detect clutter and semantically segment moving objects in the data from automotive radar sensors. In contrast to the usual practice of handling…
Continue readingThe paper introduces a method for predicting the performance of convolutional long short-term memory networks, particularly in tasks that require semantic segmentation. The method involves estimating predictive quality based on…
Continue readingThe article discusses the vulnerability of deep neural networks, specifically in semantic segmentation tasks, to adversarial attacks – perturbations of the input that lead to incorrect predictions. The authors propose…
Continue readingThis paper revisits the concept of distilling 2D features into 3D networks for autonomous driving applications. The authors propose a new approach for semantic segmentation that significantly improves upon previous…
Continue readingThis research paper introduces a U-Net-based network that uses spatio-temporal features with cross-attention transformers to extract information from historical maps. This method addresses the challenges of data-dependent uncertainty in the…
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