9 papers
DisasterRoute: Evidence-Guided Dual-System Routing for Visual Disaster Question Answering
Tan Zhang, Xing Deng, Lu Zhang +2
Visual question answering from low-altitude UAV imagery must deliver timely and accurate judgments to support disaster response under limited edge computing resources. Routine ques…
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…
Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis
Shangbo Yuan, Jie Xu, Ping Hu +2
Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph struc…
Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background
Tianyu Li, Zongqian Wu, Songyue Cai +2
CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution (OOD) detection performance. How…
Background Prompt for Few-Shot Out-of-Distribution Detection
Songyue Cai, Zongqian Wu, Yujie Mo +4
Existing foreground-background (FG-BG) decomposition methods for the few-shot out-of-distribution (FS-OOD) detection often suffer from low robustness due to over-reliance on the lo…
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…
Understanding and Mitigating Under-Confidence in GNNs from the Final Layer
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…
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation
Yujing Liu, Zongqian Wu, Zhengyu Lu +4
Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, w…
Adaptive Multi-Modality Prompt Learning
Zongqian Wu, Yujing Liu, Mengmeng Zhan +3
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, the…