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Jindong Gu

According to our database¹, Jindong Gu authored at least 59 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2024

As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?

[BibT_eX]

[DOI]

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Francesco Pinto

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Konstantinos Kamnitsas

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CoRR, 2024

An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models.

[BibT_eX]

[DOI]

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CoRR, 2024

Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds.

[BibT_eX]

[DOI]

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CoRR, 2024

Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model.

[BibT_eX]

[DOI]

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CoRR, 2024

Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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CoRR, 2024

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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CoRR, 2024

Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression.

[BibT_eX]

[DOI]

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Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Does Few-Shot Learning Suffer from Backdoor Attacks?

[BibT_eX]

[DOI]

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Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning.

[BibT_eX]

[DOI]

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Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

XAI for In-hospital Mortality Prediction via Multimodal ICU Data.

[BibT_eX]

[DOI]

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CoRR, 2023

Initialization Matters for Adversarial Transfer Learning.

[BibT_eX]

[DOI]

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Nicholas Carlini

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CoRR, 2023

OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization.

[BibT_eX]

[DOI]

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CoRR, 2023

TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation.

[BibT_eX]

[DOI]

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CoRR, 2023

Improving Adversarial Transferability via Model Alignment.

[BibT_eX]

[DOI]

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Amir-massoud Farahmand

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Philip H. S. Torr

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CoRR, 2023

Understanding and Improving In-Context Learning on Vision-language Models.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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CoRR, 2023

Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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CoRR, 2023

Benchmarking Robustness of Text-Image Composed Retrieval.

[BibT_eX]

[DOI]

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CoRR, 2023

SPOT! Revisiting Video-Language Models for Event Understanding.

[BibT_eX]

[DOI]

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CoRR, 2023

A Survey on Transferability of Adversarial Examples across Deep Neural Networks.

[BibT_eX]

[DOI]

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Philip H. S. Torr

CoRR, 2023

Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks.

[BibT_eX]

[DOI]

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CoRR, 2023

Boosting Fair Classifier Generalization through Adaptive Priority Reweighing.

[BibT_eX]

[DOI]

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CoRR, 2023

Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging.

[BibT_eX]

[DOI]

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CoRR, 2023

FedPop: Federated Population-based Hyperparameter Tuning.

[BibT_eX]

[DOI]

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CoRR, 2023

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

[BibT_eX]

[DOI]

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Philip H. S. Torr

CoRR, 2023

Reliable Evaluation of Adversarial Transferability.

[BibT_eX]

[DOI]

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Philip H. S. Torr

CoRR, 2023

Towards Robust Prompts on Vision-Language Models.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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CoRR, 2023

Influencer Backdoor Attack on Semantic Segmentation.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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Hengshuang Zhao

CoRR, 2023

Explainability and Robustness of Deep Visual Classification Models.

[BibT_eX]

[DOI]

CoRR, 2023

Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-event Video-Text Retrieval.

[BibT_eX]

[DOI]

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Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation.

[BibT_eX]

[DOI]

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Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Do DALL-E and Flamingo Understand Each Other?

[BibT_eX]

[DOI]

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Sahand Sharifzadeh

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Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Backdoor Defense via Adaptively Splitting Poisoned Dataset.

[BibT_eX]

[DOI]

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks.

[BibT_eX]

[DOI]

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Philip H. S. Torr

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Proceedings of the 34th British Machine Vision Conference 2023, 2023

ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations.

[BibT_eX]

[DOI]

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Hinrich Schütze

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Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Explainability and robustness of deep visual classification models.

[BibT_eX]

[DOI]

PhD thesis, 2022

CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering.

[BibT_eX]

[DOI]

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CoRR, 2022

Towards Efficient Adversarial Training on Vision Transformers.

[BibT_eX]

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Proceedings of the Computer Vision - ECCV 2022, 2022

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal.

[BibT_eX]

[DOI]

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Proceedings of the Computer Vision - ECCV 2022, 2022

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness.

[BibT_eX]

[DOI]

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Hengshuang Zhao

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Philip H. S. Torr

Proceedings of the Computer Vision - ECCV 2022, 2022

Are Vision Transformers Robust to Patch Perturbations?

[BibT_eX]

[DOI]

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Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Adversarial Examples on Segmentation Models Can be Easy to Transfer.

[BibT_eX]

[DOI]

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Hengshuang Zhao

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Philip H. S. Torr

CoRR, 2021

Simple Distillation Baselines for Improving Small Self-supervised Models.

[BibT_eX]

[DOI]

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CoRR, 2021

Attacking Adversarial Attacks as A Defense.

[BibT_eX]

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CoRR, 2021

Semantics for Global and Local Interpretation of Deep Convolutional Neural Networks.

[BibT_eX]

[DOI]

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Proceedings of the International Joint Conference on Neural Networks, 2021

Effective and Efficient Vote Attack on Capsule Networks.

[BibT_eX]

[DOI]

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Proceedings of the 9th International Conference on Learning Representations, 2021

Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning.

[BibT_eX]

[DOI]

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Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021

Capsule Network Is Not More Robust Than Convolutional Network.

[BibT_eX]

[DOI]

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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Interpretable Graph Capsule Networks for Object Recognition.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Interpretable Graph Capsule Networks for Object Recognition.

[BibT_eX]

[DOI]

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CoRR, 2020

Search for Better Students to Learn Distilled Knowledge.

[BibT_eX]

[DOI]

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Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

Improving the Robustness of Capsule Networks to Image Affine Transformations.

[BibT_eX]

[DOI]

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Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Introspective Learning by Distilling Knowledge from Online Self-explanation.

[BibT_eX]

[DOI]

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Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

Neural Network Memorization Dissection.

[BibT_eX]

[DOI]

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CoRR, 2019

Contextual Prediction Difference Analysis.

[BibT_eX]

[DOI]

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CoRR, 2019

Semantics for Global and Local Interpretation of Deep Neural Networks.

[BibT_eX]

[DOI]

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CoRR, 2019

Understanding Bias in Machine Learning.

[BibT_eX]

[DOI]

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CoRR, 2019

Saliency Methods for Explaining Adversarial Attacks.

[BibT_eX]

[DOI]

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CoRR, 2019

2018

Understanding Individual Decisions of CNNs via Contrastive Backpropagation.

[BibT_eX]

[DOI]

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Proceedings of the Computer Vision - ACCV 2018, 2018

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