Abstract: In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poetry writing, among others.

Abstract: The rapidly growing importance of machine learning (ML) applications, coupled with their ever-increasing model size and inference energy footprint, has created a strong need for specialized ...

Abstract: Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency. Meanwhile, several existing lightweight methods are difficult to achieve ...

Abstract: Existing object-level simultaneous localization and mapping (SLAM) methods often overlook the correspondence between semantic information and geometric features, resulting in a significant ...

Abstract: China has a vast territory, complex hydrogeological structures, and a diverse and variable climate. However, the sparse distribution of global navigation satellite system (GNSS) stations ...

Abstract: Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods ...

Abstract: Unified, or more formally, all-in-one image restoration has emerged as a practical and promising low-level vision task for real-world applications. In this context, the key issue lies in how ...

Abstract: Optical wireless integrated sensing and communication (OW-ISAC) is emerging as a crucial technology to complement and augment its radio-frequency counterpart. In this paper, we propose an ...

Abstract: Medical image segmentation has made significant strides with the development of basic models. Specifically, models that combine CNNs with transformers can successfully extract both local and ...

Abstract: Due to the constant reduction of semiconductor feature sizes, the charge sharing effect is getting worse. The occurrence probability of triple-node upset (TNU), which has a significant ...

Abstract: Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly ...