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Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations

Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations

21 September 2020
A. Wong
Mukund Mundhra
Stefano Soatto
    AAML
ArXivPDFHTML

Papers citing "Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations"

5 / 5 papers shown
Title
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Jenny Schmalfuss
Victor Oei
Lukas Mehl
Madlen Bartsch
Shashank Agnihotri
M. Keuper
Andrés Bruhn
31
0
0
14 May 2025
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through
  Language Descriptions
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
Ziyao Zeng
Yangchao Wu
Hyoungseob Park
Daniel Wang
Fengyu Yang
Stefano Soatto
Dong Lao
Byung-Woo Hong
Alex Wong
MDE
28
7
0
03 Oct 2024
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network
  via Disparity Plane Sweep
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep
Jae Young Lee
Woonghyun Ka
Jaehyun Choi
Junmo Kim
20
1
0
22 Jan 2024
Not Just Streaks: Towards Ground Truth for Single Image Deraining
Not Just Streaks: Towards Ground Truth for Single Image Deraining
Yunhao Ba
Howard Zhang
Ethan Yang
Akira Suzuki
Arnold Pfahnl
...
C. Melo
Suya You
Stefano Soatto
A. Wong
A. Kadambi
38
39
0
22 Jun 2022
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
296
3,112
0
04 Nov 2016
1