168 citations · 726 across the 30 of their papers we have counts for
6 papers · 1 filter
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
Siyuan Qiao, Zhe Lin, Jianming Zhang +1
In this paper, we study the problem of improving computational resource utilization of neural networks. Deep neural networks are usually over-parameterized for their tasks in order…
Robust Face Detection via Learning Small Faces on Hard Images
Zhishuai Zhang, Wei Shen, Siyuan Qiao +3
Recent anchor-based deep face detectors have achieved promising performance, but they are still struggling to detect hard faces, such as small, blurred and partially occluded faces…
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Xutong Ren, Lingxi Xie, Chen Wei +6
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F)…
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
Chenglin Yang, Lingxi Xie, Siyuan Qiao +1
We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with t…
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Yan Wang, Lingxi Xie, Siyuan Qiao +3
Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recogniti…
Deep Co-Training for Semi-Supervised Image Recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang +2
In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep…