Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range-Reference-Cited by-同舟云学术

Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range

Published:2024-08-24 Issue: Volume:33 Page:2596-2607
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Shao Huaqing¹^ORCID,Wang Lanjun²^ORCID,Wang Yongwei³^ORCID,Ren Qibing⁴^ORCID,Yan Junchi⁵^ORCID

Affiliation:

1. Department of CSE and MoE Key Lab of AI, Shanghai Jiao Tong University, Shanghai, China

2. SNMC, Tianjin University, Tianjin, China

3. SIAS and College of Computer Science, Zhejiang University, Hangzhou, China

4. Department of CSE, Shanghai Jiao Tong University, Shanghai, China

5. School of AI and Department of CSE, Shanghai Jiao Tong University, Shanghai, China

Funder

Shanghai Municipal Science and Technology Major Project

National Natural Science Foundation of China

Shanghai Pujiang Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671852

Reference46 articles.

1. Motasem Alfarra, Adel Bibi, Philip Torr, and Bernard Ghanem. 2022. Data Dependent Randomized Smoothing. In The 38th Conference on Uncertainty in Artificial Intelligence.

2. Machine learning for combinatorial optimization: A methodological tour d’horizon

3. Aleksandar Bojchevski, Johannes Klicpera, and Stephan Günnemann. 2020. Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more. In International Conference on Machine Learning. PMLR, 1003--1013.

4. Poselets: Body part detectors trained using 3D human pose annotations

5. Detection as regression: Certified object detection with median smoothing;Curry Michael;Advances in Neural Information Processing Systems,2020