5 papers
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
Xin Zhou, Dingkang Liang, Wei Xu +4
Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However, existing methods for model adaptation usually update all model pa…
Anomaly Detection by Adapting a pre-trained Vision Language Model
Yuxuan Cai, Xinwei He, Dingkang Liang +2
Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for An…
You Only Look Bottom-Up for Monocular 3D Object Detection
Kaixin Xiong, Dingyuan Zhang, Dingkang Liang +5
Monocular 3D Object Detection is an essential task for autonomous driving. Meanwhile, accurate 3D object detection from pure images is very challenging due to the loss of depth inf…
A Discrepancy Aware Framework for Robust Anomaly Detection
Yuxuan Cai, Dingkang Liang, Dongliang Luo +3
Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although ma…
Diffusion-based 3D Object Detection with Random Boxes
Xin Zhou, Jinghua Hou, Tingting Yao +6
3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the…