270 citations · 805 across the 14 of their papers we have counts for
13 papers · 1 filter
Variational Pedestrian Detection
Yuang Zhang, Huanyu He, Jianguo Li +3
Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the curren…
AP-Loss for Accurate One-Stage Object Detection
Kean Chen, Weiyao Lin, Jianguo Li +3
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background cla…
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
Shuyuan Li, Jianguo Li, Hanlin Tang +2
Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while ap…
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…
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…
Deep Attributes from Context-Aware Regional Neural Codes
Jianwei Luo, Jianguo Li, Jun Wang +2
Recently, many researches employ middle-layer output of convolutional neural network models (CNN) as features for different visual recognition tasks. Although promising results hav…
Large-scale Supervised Hierarchical Feature Learning for Face Recognition
Jianguo Li, Yurong Chen
This paper proposes a novel face recognition algorithm based on large-scale supervised hierarchical feature learning. The approach consists of two parts: hierarchical feature learn…