5 papers
Towards Generalized Range-View LiDAR Segmentation in Adverse Weather
Longyu Yang, Lu Zhang, Jun Liu +4
LiDAR segmentation has emerged as an important task to enrich scene perception and understanding. Range-view-based methods have gained popularity due to their high computational ef…
Towards Explicit Geometry-Reflectance Collaboration for Generalized LiDAR Segmentation in Adverse Weather
Longyu Yang, Ping Hu, Shangbo Yuan +4
Existing LiDAR semantic segmentation models often suffer from decreased accuracy when exposed to adverse weather conditions. Recent methods addressing this issue focus on enhancing…
On Efficient Variants of Segment Anything Model: A Survey
Xiaorui Sun, Jun Liu, Heng Tao Shen +2
The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive perf…
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees
Zhiyuan Wang, Jinhao Duan, Lu Cheng +6
Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (L…
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models
Fei Kong, Jinhao Duan, Lichao Sun +6
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds. Consistency training addresses this issue with single-step sampling…