1 citations · 2 across the 2 of their papers we have counts for
6 papers
Location-aware Upsampling for Semantic Segmentation
Xiangyu He, Zitao Mo, Qiang Chen +3
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks. Beyond these semantic metrics,…
A System-Level Solution for Low-Power Object Detection
Fanrong Li, Zitao Mo, Peisong Wang +8
Object detection has made impressive progress in recent years with the help of deep learning. However, state-of-the-art algorithms are both computation and memory intensive. Though…
SpatialFlow: Bridging All Tasks for Panoptic Segmentation
Qiang Chen, Anda Cheng, Xiangyu He +2
Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues fo…
Compact Global Descriptor for Neural Networks
Xiangyu He, Ke Cheng, Qiang Chen +3
Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convo…
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks
Jian Cheng, Peisong Wang, Gang Li +2
Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational c…
From Hashing to CNNs: Training BinaryWeight Networks via Hashing
Qinghao Hu, Peisong Wang, Jian Cheng
Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to realworld appli…