5 papers
CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
Mengke Li, Haiquan Ling, Lihao Chen +3
Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these is…
PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought
Chaoqi Chen, Qile Xu, Wenjun Zhou +1
Understanding 3D point clouds through language remains a fundamental challenge in computer graphics and visual computing, due to the irregular structure of point cloud data and the…
Back to Source: Open-Set Continual Test-Time Adaptation via Domain Compensation
Yingkai Yang, Chaoqi Chen, Hui Huang
Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic…
TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
Yuxiang Zhong, Jun Wei, Chaoqi Chen +2
3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, th…
Out-of-Distribution Detection with Prototypical Outlier Proxy
Mingrong Gong, Chaoqi Chen, Qingqiang Sun +2
Out-of-distribution (OOD) detection is a crucial task for deploying deep learning models in the wild. One of the major challenges is that well-trained deep models tend to perform o…