2.5k citations · 2.7k across the 7 of their papers we have counts for
7 papers
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Jiang Liu, Alexander Levine, Chun Pong Lau +2
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of…
Attention Mechanisms in Computer Vision: A Survey
Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu +7
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim o…
Centralized Information Interaction for Salient Object Detection
Jiang-Jiang Liu, Zhi-Ang Liu, Ming-Ming Cheng
The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. However, most existing U-shape based methods focused on im…
EGNet:Edge Guidance Network for Salient Object Detection
Jia-Xing Zhao, Jiangjiang Liu, Den-Ping Fan +3
Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse obje…
WebSeg: Learning Semantic Segmentation from Web Searches
Qibin Hou, Ming-Ming Cheng, Jiangjiang Liu +1
In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations,…
Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction
Qibin Hou, Jiang-Jiang Liu, Ming-Ming Cheng +2
In this paper, we aim at solving pixel-wise binary problems, including salient object segmentation, skeleton extraction, and edge detection, by introducing a unified architecture.…
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu +3
We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image…