Search

Your search keyword '"Yong, Jun-Hai"' showing total 151 results
Start Over Author "Yong, Jun-Hai" Language english
151 results on '"Yong, Jun-Hai"'

9. Focused and Collaborative Feedback Integration for Interactive Image Segmentation

Author

Wei, Qiaoqiao, Zhang, Hui, and Yong, Jun-Hai

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Interactive image segmentation aims at obtaining a segmentation mask for an image using simple user annotations. During each round of interaction, the segmentation result from the previous round serves as feedback to guide the user's annotation and provides dense prior information for the segmentation model, effectively acting as a bridge between interactions. Existing methods overlook the importance of feedback or simply concatenate it with the original input, leading to underutilization of feedback and an increase in the number of required annotations. To address this, we propose an approach called Focused and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for click-based interactive image segmentation. FCFI first focuses on a local area around the new click and corrects the feedback based on the similarities of high-level features. It then alternately and collaboratively updates the feedback and deep features to integrate the feedback into the features. The efficacy and efficiency of FCFI were validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved new state-of-the-art performance with less computational overhead than previous methods. The source code is available at https://github.com/veizgyauzgyauz/FCFI., Accepted for publication at CVPR 2023

Published
2023

18. Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Author

Ma, Chen, Guo, Xiangyu, Chen, Li, Yong, Jun-Hai, and Wang, Yisen

Subjects
FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Cryptography and Security (cs.CR)
Abstract

One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack. Assuming the decision boundary is locally flat, we theoretically prove that the minimum $\ell_2$ distortion can be obtained by reaching the decision boundary along the tangent line passing through such tangent point in each iteration. To improve the robustness of our method, we further propose a generalized method which replaces the hemisphere with a semi-ellipsoid to adapt to curved decision boundaries. Our approach is free of pre-training. Extensive experiments conducted on the ImageNet and CIFAR-10 datasets demonstrate that our approach can consume only a small number of queries to achieve the low-magnitude distortion. The implementation source code is released online at https://github.com/machanic/TangentAttack., Accepted at NeurIPS 2021. The missing square term in Eqn.(13), as well as many other mistakes of the previous version, have been fixed in the current version

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results