4 papers
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…
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…
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…