7 accepted papers
Reliable cognitive load (CL) prediction in real-world settings is fundamentally constrained by the poor and variable quality of physiological signals. In safety-critical tasks such as driving, degraded signal quality can severely compromise prediction accuracy, limiting the deployment of existing mo…
Underwater stereo depth estimation provides accurate 3D geometry for robotics tasks such as navigation, inspection, and mapping, offering metric depth from low-cost passive cameras while avoiding the scale ambiguity of monocular methods. However, existing approaches face two critical challenges: (i)…
Image super-resolution (SR) is essential for bridging the gap between modern hardware and real-time computer graphics (CG) applications. It reduces CG workload by allowing low-resolution rendering, with original quality restored later via mathematical operations or machine learning. However, recent…
Natural language generation is an important task in natural language processing and has been applied in various scenarios. Most state-of-the-art generation models, however, are usually slow at inference time mainly due to the sequential dependencies of autoregressive generation and the use of more a…
Autonomous driving systems rely on LiDAR-based 3D object detection to identify obstacles. Recent studies have shown that detectors are susceptible to disappearing attacks, leading to missed detections and potential vehicle collisions. However, improving the adversarial robustness of 3D object detect…
Online semantic 3D modeling from streaming RGB-D data fundamentally requires consistent fusion of 2D segmentation. Popular approaches address segmentation inconsistencies through histogram-based label aggregation, where each 3D element (point/voxel) maintains the frequency of candidate labels, which
We introduce the Safe Offline-to-Online Multi-Agent Decision Transformer (SO2-MADT), an innovative framework that revolutionizes safety considerations in Multi-agent Reinforcement Learning (MARL) through a novel sequence modeling approach. Leveraging the dynamic capabilities inherent in Decision Tra…