1 citations · 1 across the 10 of their papers we have counts for
6 papers · 1 filter
Deep Lookup Network
Yulan Guo, Longguang Wang, Wendong Mao +4
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operatio…
StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud
Weichao Wang, Wendong Mao, Zhongfeng Wang
The deployment of high-accuracy 3D object detection models from point cloud remains a significant challenge due to their substantial computational and memory requirements. To addre…
A Memory-Efficient Framework for Deformable Transformer with Neural Architecture Search
Wendong Mao, Mingfan Zhao, Jianfeng Guan +2
Deformable Attention Transformers (DAT) have shown remarkable performance in computer vision tasks by adaptively focusing on informative image regions. However, their data-dependen…
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
Huihong Shi, Haikuo Shao, Wendong Mao +1
Motivated by the huge success of Transformers in the field of natural language processing (NLP), Vision Transformers (ViTs) have been rapidly developed and achieved remarkable perf…
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Minghao She, Wendong Mao, Huihong Shi +1
Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance dro…
Semi-dense Stereo Matching using Dual CNNs
Wendong Mao, Mingjie Wang, Jun Zhou +1
A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively…