270 citations · 805 across the 14 of their papers we have counts for
5 papers · 1 filter
Composite Binary Decomposition Networks
You Qiaoben, Zheng Wang, Jianguo Li +3
Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precisi…
Object Detection from Scratch with Deep Supervision
Zhiqiang Shen, Zhuang Liu, Jianguo Li +3
We propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-…
Network Decoupling: From Regular to Depthwise Separable Convolutions
Jianbo Guo, Yuxi Li, Weiyao Lin +2
Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first anal…
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Yuxi Li, Jiuwei Li, Weiyao Lin +1
Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, whic…
Learning Visual Knowledge Memory Networks for Visual Question Answering
Zhou Su, Chen Zhu, Yinpeng Dong +3
Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly answered from visual conten…