activity
20142023
most citedRobust Real-World Image Super-Resolution against Adversarial Attacks

923 citations · 1.8k across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2022923 cited

Robust Real-World Image Super-Resolution against Adversarial Attacks

Jiutao Yue, Haofeng Li, Pengxu Wei +2

Recently deep neural networks (DNNs) have achieved significant success in real-world image super-resolution (SR). However, adversarial image samples with quasi-imperceptible noises…

cs.CV20223 cited

Adversarially-Aware Robust Object Detector

Ziyi Dong, Pengxu Wei, Liang Lin

Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks. Nevertheless, few works explore the adversar…

cs.CV20228 cited

The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network

Zhongzhan Huang, Senwei Liang, Mingfu Liang +3

Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural netwo…

cs.CV2017676 cited

Cost-Effective Active Learning for Deep Image Classification

Keze Wang, Dongyu Zhang, Ya Li +2

Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In t…

cs.CV20167 cited

Is Faster R-CNN Doing Well for Pedestrian Detection?

Liliang Zhang, Liang Lin, Xiaodan Liang +1

Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN [1, 2]…

cs.CV20162 cited

Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning

Yukai Shi, Keze Wang, Li Xu +1

Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patc…