270 citations · 805 across the 14 of their papers we have counts for
4 papers · 1 filter
Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen +3
The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel…
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
Yinpeng Dong, Renkun Ni, Jianguo Li +3
Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However, they suffer much from the non-…
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Zhiqiang Shen, Zhuang Liu, Jianguo Li +3
We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off-the-shelf n…
Weakly Supervised Dense Video Captioning
Zhiqiang Shen, Jianguo Li, Zhou Su +4
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption s…