539 citations · 2k across the 53 of their papers we have counts for
8 papers · 2 filters
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
Weiyao Lin, Yang Mi, Jianxin Wu +2
Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recogn…
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
Object detection aims at high speed and accuracy simultaneously. However, fast models are usually less accurate, while accurate models cannot satisfy our need for speed. A fast mod…
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Xiu-Shen Wei, Chen-Lin Zhang, Jianxin Wu +2
Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications. In this paper, we focus on the reusability of pre-trained dee…
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Jian-Hao Luo, Jianxin Wu, Weiyao Lin
We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter lev…
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as t…
An Entropy-based Pruning Method for CNN Compression
Jian-Hao Luo, Jianxin Wu
This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-b…
Deep Descriptor Transforming for Image Co-Localization
Xiu-Shen Wei, Chen-Lin Zhang, Yao Li +4
Reusable model design becomes desirable with the rapid expansion of machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional mode…
Learning Correspondence Structures for Person Re-identification
Weiyao Lin, Yang Shen, Junchi Yan +4
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-ba…