26 citations · 52 across the 6 of their papers we have counts for
7 papers
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He +7
Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\e…
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond
Xiangyu He, Jian Cheng
Super-resolution as an ill-posed problem has many high-resolution candidates for a low-resolution input. However, the popular loss used to best fit the given HR image fail…
Generative Zero-shot Network Quantization
Xiangyu He, Qinghao Hu, Peisong Wang +1
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level im…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
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…
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…