317 citations · 1.4k across the 24 of their papers we have counts for
24 papers
Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
Yichao Yan, Jie Qin1, Jiaxin Chen +4
Video-based person re-identification (re-ID) is an important research topic in computer vision. The key to tackling the challenging task is to exploit both spatial and temporal clu…
Learning Multi-Attention Context Graph for Group-Based Re-Identification
Yichao Yan, Jie Qin, Bingbing Ni +6
Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on…
Anchor-Free Person Search
Yichao Yan, Jinpeng Li, Jie Qin +5
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and pe…
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Bing Wang, Changhao Chen, Zhaopeng Cui +8
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D loc…
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Lei Huang, Jie Qin, Yi Zhou +3
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various app…
Group Whitening: Balancing Learning Efficiency and Representational Capacity
Lei Huang, Yi Zhou, Li Liu +2
Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a m…
On the Number of Linear Regions of Convolutional Neural Networks
H. Xiong, L. Huang, M. Yu +3
One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that…
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
Ionut Cosmin Duta, Li Liu, Fan Zhu +1
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales. PyConv contains a pyramid of kernels, where each level invo…
Improved Residual Networks for Image and Video Recognition
Ionut Cosmin Duta, Li Liu, Fan Zhu +1
Residual networks (ResNets) represent a powerful type of convolutional neural network (CNN) architecture, widely adopted and used in various tasks. In this work we propose an impro…
Controllable Orthogonalization in Training DNNs
Lei Huang, Li Liu, Fan Zhu +4
Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in represe…
An Investigation into the Stochasticity of Batch Whitening
Lei Huang, Lei Zhao, Yi Zhou +3
Batch Normalization (BN) is extensively employed in various network architectures by performing standardization within mini-batches. A full understanding of the process has been a…
Auto-Encoding Twin-Bottleneck Hashing
Yuming Shen, Jie Qin, Jiaxin Chen +5
Conventional unsupervised hashing methods usually take advantage of similarity graphs, which are either pre-computed in the high-dimensional space or obtained from random anchor po…
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs
Lei Huang, Jie Qin, Li Liu +2
Conditioning analysis uncovers the landscape of an optimization objective by exploring the spectrum of its curvature matrix. This has been well explored theoretically for linear mo…
RANet: Ranking Attention Network for Fast Video Object Segmentation
Ziqin Wang, Jun Xu, Li Liu +2
Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their pr…
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent
Huan Xiong, Mengyang Yu, Li Liu +3
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and ma…
Embarrassingly Simple Binary Representation Learning
Yuming Shen, Jie Qin, Jiaxin Chen +2
Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries. However, one may wonder…
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
Jun Xu, Yuan Huang, Ming-Ming Cheng +4
Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and clean images. Unsupervised…
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Yingkun Hou, Jun Xu, Mingxia Liu +4
Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS pri…
STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren +5
Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives a…
Dynamically Visual Disambiguation of Keyword-based Image Search
Yazhou Yao, Zeren Sun, Fumin Shen +6
Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy.…
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Syed Waqas Zamir, Aditya Arora, Akshita Gupta +7
Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation i…
Iterative Normalization: Beyond Standardization towards Efficient Whitening
Lei Huang, Yi Zhou, Fan Zhu +2
Batch Normalization (BN) is ubiquitously employed for accelerating neural network training and improving the generalization capability by performing standardization within mini-bat…
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Zheng Zhang, Li Liu, Jie Qin +5
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-econom…
Towards Automatic Construction of Diverse, High-quality Image Dataset
Yazhou Yao, Jian Zhang, Fumin Shen +4
The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models develo…