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07/14/2023
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
It is imperative to ensure the robustness of deep learning models in cri...
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07/13/2023
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Recent video recognition models utilize Transformer models for long-rang...
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06/16/2023
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
The success of deep learning based face recognition systems has given ri...
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06/15/2023
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Hybrid volumetric medical image segmentation models, combining the advan...
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04/06/2023
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Adopting contrastive image-text pretrained models like CLIP towards vide...
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02/23/2023
Boosting Adversarial Transferability using Dynamic Cues
The transferability of adversarial perturbations between image models ha...
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12/30/2022
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Adversarial training is an effective approach to make deep neural networ...
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12/11/2022
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
Although existing semi-supervised learning models achieve remarkable suc...
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10/13/2022
How to Train Vision Transformer on Small-scale Datasets?
Vision Transformer (ViT), a radically different architecture than convol...
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07/25/2022
Self-Distilled Vision Transformer for Domain Generalization
In recent past, several domain generalization (DG) methods have been pro...
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07/18/2022
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Transferable adversarial attacks optimize adversaries from a pretrained ...
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12/02/2021
Self-supervised Video Transformer
In this paper, we propose self-supervised training for video transformer...
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06/08/2021
On Improving Adversarial Transferability of Vision Transformers
Vision transformers (ViTs) process input images as sequences of patches ...
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04/26/2021
Rich Semantics Improve Few-shot Learning
Human learning benefits from multi-modal inputs that often appear as ric...
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03/26/2021
On Generating Transferable Targeted Perturbations
While the untargeted black-box transferability of adversarial perturbati...
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03/25/2021
Orthogonal Projection Loss
Deep neural networks have achieved remarkable performance on a range of ...
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01/04/2021
Transformers in Vision: A Survey
Astounding results from transformer models on natural language tasks hav...
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07/29/2020
Stylized Adversarial Defense
Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, ...
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06/08/2020
A Self-supervised Approach for Adversarial Robustness
Adversarial examples can cause catastrophic mistakes in Deep Neural Netw...
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05/28/2019
Cross-Domain Transferability of Adversarial Perturbations
Adversarial examples reveal the blind spots of deep neural networks (DNN...
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11/22/2018
Distorting Neural Representations to Generate Highly Transferable Adversarial Examples
Deep neural networks (DNN) can be easily fooled by adding human impercep...
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07/03/2018
Local Gradients Smoothing: Defense against localized adversarial attacks
Deep neural networks (DNNs) have shown vulnerability to adversarial atta...
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03/09/2018