5 papers
DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments
Tan Zhang, Quanyou Li, Lu Zhang +3
When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude U…
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…
The Final Layer Holds the Key: A Unified and Efficient GNN Calibration Framework
Jincheng Huang, Jie Xu, Xiaoshuang Shi +3
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalibrated, typically exhibiting unde…
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…