1.1k citations · 2.6k across the 75 of their papers we have counts for
15 papers · 1 filter
LiDAR Iris for Loop-Closure Detection
Ying Wang, Zezhou Sun, Cheng-Zhong Xu +3
In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained fo…
Dual-Attention Graph Convolutional Network
Xueya Zhang, Tong Zhang, Wenting Zhao +2
Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges…
AIM 2019 Challenge on Image Demoireing: Methods and Results
Shanxin Yuan, Radu Timofte, Gregory Slabaugh +25
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper desc…
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
Sheng Wan, Chen Gong, Ping Zhong +3
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based metho…
Multi-scale Dynamic Feature Encoding Network for Image Demoireing
Xi Cheng, Zhenyong Fu, Jian Yang
The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photo…
Image Formation Model Guided Deep Image Super-Resolution
Jinshan Pan, Yang Liu, Deqing Sun +4
We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. The proposed algor…
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
Chun-Mei Feng, Yong Xu, Zuoyong Li +1
Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the mi…
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
Zhenyu Zhang, Zhen Cui, Chunyan Xu +3
In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation. The motivation behind it comes…
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
Sheng Wan, Chen Gong, Ping Zhong +3
Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. How…
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Zhenyu Zhang, Stéphane Lathuilière, Andrea Pilzer +3
In this work, we tackle the problem of online adaptation for stereo depth estimation, that consists in continuously adapting a deep network to a target video recordedin an environm…
Ensemble Teaching for Hybrid Label Propagation
Chen Gong, Dacheng Tao, Xiaojun Chang +1
Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot…
A Regularization Approach for Instance-Based Superset Label Learning
Chen Gong, Tongliang Liu, Yuanyan Tang +3
Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training e…
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
Lei Zhang, Shanshan Wang, Guang-Bin Huang +3
In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e. non-i.i.d.). Maximum mean discrepancy (MMD), as a do…
Selective Kernel Networks
Xiang Li, Wenhai Wang, Xiaolin Hu +1
In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience…
Learning with Inadequate and Incorrect Supervision
Chen Gong, Hengmin Zhang, Jian Yang +1
Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistaken…