activity
20202023
most citedCross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

92 citations · 170 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV2023★ 9 cited

Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers

Zhou Huang, Hang Dai, Tian-Zhu Xiang +4

Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective…

cs.CV2023★ 2 cited

MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving

Jiale Li, Hang Dai, Hao Han +1

LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small…

cs.CV2022★ 92 cited

Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

Yu Hong, Hang Dai, Yong Ding

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing met…

cs.CV2022★ 2 cited

Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks

Yunqing Bao, Hang Dai, Abdulmotaleb Elsaddik

Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic in…

cs.CV2022★ 7 cited

Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

Yi-Nan Chen, Hang Dai, Yong Ding

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-ba…

cs.CV2022★ 13 cited

High-resolution Iterative Feedback Network for Camouflaged Object Detection

Xiaobin Hu, Shuo Wang, Xuebin Qin +5

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the…