Publications (5)
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
Linpu Fang, Xingyan Liu, Li Liu +2
State-of-the-art single depth image-based 3D hand pose estimation methods are based on dense predictions, including voxel-to-voxel predictions, point-to-point regression, and pixel…
EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
Linpu Fang, Hang Xu, Zhili Liu +2
Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detecto…
Dynamic Group Convolution for Accelerating Convolutional Neural Networks
Zhuo Su, Linpu Fang, Wenxiong Kang +3
Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted i…
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond
Zhuo Su, Linpu Fang, Deke Guo +3
Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated…
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
Hang Xu, Linpu Fang, Xiaodan Liang +2
The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this…