5 accepted papers
Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, but their enormous memory footprints pose significant challenges for deployment on consumer-grade GPUs. Prior solutions, such as PowerInfer, combine offloading and sparse activation to reduce memory and computational…
Navigating domain shifts in object detection is crucial for autonomous driving systems, particularly under varying weather conditions and diverse visual perspectives. Existing Cross-Domain Object Detection methods often struggle due to their reliance on broad semantic models, which can introduce bia…
Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous l…
Despite the growing integration of deep models into mobile terminals, the accuracy of these models declines significantly due to various deployment interferences. Test-time adaptation (TTA) has emerged to improve the performance of deep models by adapting them to unlabeled target data online. Yet, t…
Dynamic state representation learning is essential for robot learning. Good latent space that can accurately describe dynamic transition and constraints can significantly accelerate reinforcement learning training as well as reduce motion planning complexity. However, deformable object have very com