140 citations · 265 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Tao Kong, Fuchun Sun, Anbang Yao +3
We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and regi…
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Hao Zhao, Ming Lu, Anbang Yao +3
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is seman…
Network Sketching: Exploiting Binary Structure in Deep CNNs
Yiwen Guo, Anbang Yao, Hao Zhao +1
Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are typically resourc…
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…