Chaithanya Kumar Mummadi
Chaithanya Kumar Mummadi
Machine Learning Research Scientist, Bosch Center for Artificial Intelligence, Robert Bosch LLC, USA
Verified email at
Cited by
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Universal adversarial perturbations against semantic image segmentation
J Hendrik Metzen, M Chaithanya Kumar, T Brox, V Fischer
Proceedings of the IEEE international conference on computer vision, 2755-2764, 2017
Self: Learning to filter noisy labels with self-ensembling
DT Nguyen, CK Mummadi, TPN Ngo, THP Nguyen, L Beggel, T Brox
arXiv preprint arXiv:1910.01842, 2019
Adversarial examples for semantic image segmentation
V Fischer, MC Kumar, JH Metzen, T Brox
arXiv preprint arXiv:1703.01101, 2017
Deepusps: Deep robust unsupervised saliency prediction via self-supervision
T Nguyen, M Dax, CK Mummadi, N Ngo, THP Nguyen, Z Lou, T Brox
Advances in Neural Information Processing Systems 32, 2019
Real-time and embedded detection of hand gestures with an IMU-based glove
CK Mummadi, F Philips Peter Leo, K Deep Verma, S Kasireddy, ...
Informatics 5 (2), 28, 2018
Defending against universal perturbations with shared adversarial training
CK Mummadi, T Brox, JH Metzen
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
Test-time adaptation to distribution shift by confidence maximization and input transformation
CK Mummadi, R Hutmacher, K Rambach, E Levinkov, T Brox, JH Metzen
arXiv preprint arXiv:2106.14999, 2021
Does enhanced shape bias improve neural network robustness to common corruptions?
CK Mummadi, R Subramaniam, R Hutmacher, J Vitay, V Fischer, ...
arXiv preprint arXiv:2104.09789, 2021
Real-time embedded recognition of sign language alphabet fingerspelling in an imu-based glove
CK Mummadi, FPP Leo, KD Verma, S Kasireddy, PM Scholl, ...
Proceedings of the 4th international Workshop on Sensor-based Activity …, 2017
Method and device for improving the robustness against “adversarial examples”
CK Mummadi, JH Metzen, V Fischer
US Patent 11,055,632, 2021
Give me your attention: Dot-product attention considered harmful for adversarial patch robustness
G Lovisotto, N Finnie, M Munoz, CK Mummadi, JH Metzen
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Diagvib-6: A diagnostic benchmark suite for vision models in the presence of shortcut and generalization opportunities
E Eulig, P Saranrittichai, CK Mummadi, K Rambach, W Beluch, X Shi, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Group Pruning Using a Bounded- Norm for Group Gating and Regularization
CK Mummadi, T Genewein, D Zhang, T Brox, V Fischer
German Conference on Pattern Recognition, 139-155, 2019
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain
P Saranrittichai, CK Mummadi, C Blaiotta, M Munoz, V Fischer
European Conference on Computer Vision, 294-309, 2022
Multi-attribute Open Set Recognition
P Saranrittichai, CK Mummadi, C Blaiotta, M Munoz, V Fischer
DAGM German Conference on Pattern Recognition, 101-115, 2022
Zero-Shot Visual Classification with Guided Cropping
P Saranrittichai, M Munoz, V Fischer, CK Mummadi
arXiv preprint arXiv:2309.06581, 2023
Device and method for determining adversarial patches for a machine learning system
AMM Delgado, CK Mummadi, G Lovisotto, JH Metzen, NY Finnie
US Patent App. 18/163,681, 2023
More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes
B An, S Zhu, MA Panaitescu-Liess, CK Mummadi, F Huang
arXiv preprint arXiv:2308.01313, 2023
More Context, Less Distraction: Improving Zero-Shot Inference of CLIP by Inferring and Describing Spurious Features
B An, S Zhu, MA Panaitescu-Liess, CK Mummadi, F Huang
Workshop on Efficient Systems for Foundation Models@ ICML2023, 2023
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
A Saha, S Yu, A Norouzzadeh, WY Lin, CK Mummadi
arXiv preprint arXiv:2306.12610, 2023
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