Jindong Gu
Jindong Gu
University of Oxford & Google Research
Verified email at - Homepage
Cited by
Cited by
Understanding individual decisions of cnns via contrastive backpropagation
J Gu, Y Yang, V Tresp
Proceedings of the Asian Conference on Computer Vision (ACCV), 119-134, 2018
Improving the robustness of capsule networks to image affine transformations
J Gu, V Tresp
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7285-7293, 2020
Are vision transformers robust to patch perturbations?
J Gu, V Tresp, Y Qin
European Conference on Computer Vision (ECCV), 404-421, 2022
Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness
J Gu, H Zhao, V Tresp, PHS Torr
European Conference on Computer Vision (ECCV), 308-325, 2022
Saliency methods for explaining adversarial attacks
J Gu, V Tresp
Workshop on Human-Centric Machine Learning, NeurIPS 2019, 2019
Interpretable graph capsule networks for object recognition
J Gu
Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 1469-1477, 2021
Effective and Efficient Vote Attack on Capsule Networks
J Gu, B Wu, V Tresp
International Conference on Learning Representations (ICLR), 2021, 2021
Capsule network is not more robust than convolutional network
J Gu, V Tresp, H Hu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 14309-14317, 2021
Towards efficient adversarial training on vision transformers
B Wu*, J Gu*, Z Li, D Cai, X He, W Liu
European Conference on Computer Vision (ECCV), 307-325, 2022
Understanding bias in machine learning
J Gu, D Oelke
Workshop on Visualization for AI Explainability, IEEE Vis 2018, 2019
Attacking Adversarial Attacks as A Defense
B Wu, H Pan, L Shen, J Gu, S Zhao, Z Li, D Cai, X He, W Liu
arXiv preprint arXiv:2106.04938, 2021
Search for better students to learn distilled knowledge
J Gu, V Tresp
European Conference on Artificial Intelligence (ECAI), 1159-1165, 2020
Semantics for global and local interpretation of deep neural networks
J Gu, V Tresp
arXiv preprint arXiv:1910.09085, 2019
Adversarial examples on segmentation models can be easy to transfer
J Gu, H Zhao, V Tresp, P Torr
arXiv preprint arXiv:2111.11368, 2021
Contextual prediction difference analysis for explaining individual image classifications
J Gu, V Tresp
arXiv preprint arXiv:1910.09086, 2019
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
J Gu, Z Han, S Chen, A Beirami, B He, G Zhang, R Liao, Y Qin, V Tresp, ...
arXiv preprint arXiv:2307.12980, 2023
Watermark vaccine: Adversarial attacks to prevent watermark removal
X Liu, J Liu, Y Bai, J Gu, T Chen, X Jia, X Cao
European Conference on Computer Vision (ECCV), 1-17, 2022
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Z Han, R Liao, J Gu, Y Zhang, Z Ding, Y Gu, H Köppl, H Schütze, V Tresp
Findings of the Association for Computational Linguistics: ACL 2023, 2022
Neural network memorization dissection
J Gu, V Tresp
Workshop on Machine Learning with Guarantees, NeurIPS 2019, 2019
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
S Chen*, J Gu*, Z Han, Y Ma, P Torr, V Tresp
arXiv preprint arXiv:2306.02080, 2023
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