research
∙
09/15/2023
Adaptive Priority Reweighing for Generalizing Fairness Improvement
With the increasing penetration of machine learning applications in crit...
share
research
∙
09/12/2023
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks
Deep Neural Networks can be easily fooled by small and imperceptible per...
research
∙
08/22/2023
Multi-event Video-Text Retrieval
Video-Text Retrieval (VTR) is a crucial multi-modal task in an era of ma...
research
∙
08/22/2023
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging
Fast Adversarial Training (FAT) not only improves the model robustness b...
research
∙
08/21/2023
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Recently, foundation models have exhibited remarkable advancements in mu...
research
∙
08/17/2023
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Uncertainty quantification is critical for deploying deep neural network...
research
∙
08/16/2023
FedPop: Federated Population-based Hyperparameter Tuning
Federated Learning (FL) is a distributed machine learning (ML) paradigm,...
research
∙
07/24/2023
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
Prompt engineering is a technique that involves augmenting a large pre-t...
research
∙
06/14/2023
Reliable Evaluation of Adversarial Transferability
Adversarial examples (AEs) with small adversarial perturbations can misl...
research
∙
06/03/2023
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
Various adaptation methods, such as LoRA, prompts, and adapters, have be...
research
∙
04/17/2023
Towards Robust Prompts on Vision-Language Models
With the advent of vision-language models (VLMs) that can perform in-con...
research
∙
03/23/2023
Backdoor Defense via Adaptively Splitting Poisoned Dataset
Backdoor defenses have been studied to alleviate the threat of deep neur...
research
∙
03/21/2023
Influencer Backdoor Attack on Semantic Segmentation
When a small number of poisoned samples are injected into the training d...
research
∙
01/03/2023
Explainability and Robustness of Deep Visual Classification Models
In the computer vision community, Convolutional Neural Networks (CNNs), ...
research
∙
12/23/2022
Do DALL-E and Flamingo Understand Each Other?
A major goal of multimodal research is to improve machine understanding ...
research
∙
11/19/2022
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task. To ...
research
∙
07/25/2022
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Deep neural network-based image classifications are vulnerable to advers...
research
∙
07/21/2022
Towards Efficient Adversarial Training on Vision Transformers
Vision Transformer (ViT), as a powerful alternative to Convolutional Neu...
research
∙
07/17/2022
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
As a common security tool, visible watermarking has been widely applied ...
research
∙
11/22/2021
Adversarial Examples on Segmentation Models Can be Easy to Transfer
Deep neural network-based image classification can be misled by adversar...
research
∙
11/20/2021
Are Vision Transformers Robust to Patch Perturbations?
The recent advances in Vision Transformer (ViT) have demonstrated its im...
research
∙
06/21/2021
Simple Distillation Baselines for Improving Small Self-supervised Models
While large self-supervised models have rivalled the performance of thei...
research
∙
06/09/2021
Attacking Adversarial Attacks as A Defense
It is well known that adversarial attacks can fool deep neural networks ...
research
∙
06/01/2021
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
Deep neural networks are increasingly being used for the analysis of med...
research
∙
03/29/2021
Capsule Network is Not More Robust than Convolutional Network
The Capsule Network is widely believed to be more robust than Convolutio...
research
∙
02/19/2021
Effective and Efficient Vote Attack on Capsule Networks
Standard Convolutional Neural Networks (CNNs) can be easily fooled by im...
research
∙
12/03/2020
Interpretable Graph Capsule Networks for Object Recognition
Capsule Networks, as alternatives to Convolutional Neural Networks, have...
research
∙
09/19/2020
Introspective Learning by Distilling Knowledge from Online Self-explanation
In recent years, many explanation methods have been proposed to explain ...
research
∙
01/30/2020
Search for Better Students to Learn Distilled Knowledge
Knowledge Distillation, as a model compression technique, has received g...
research
∙
11/21/2019
Neural Network Memorization Dissection
Deep neural networks (DNNs) can easily fit a random labeling of the trai...
research
∙
11/18/2019
Improving the Robustness of Capsule Networks to Image Affine Transformations
Convolutional neural networks (CNNs) achieve translational invariance us...
research
∙
10/21/2019
Contextual Prediction Difference Analysis
The interpretation of black-box models has been investigated in recent y...
research
∙
10/21/2019
Semantics for Global and Local Interpretation of Deep Neural Networks
Deep neural networks (DNNs) with high expressiveness have achieved state...
research
∙
09/02/2019
Understanding Bias in Machine Learning
Bias is known to be an impediment to fair decisions in many domains such...
research
∙
08/22/2019
Saliency Methods for Explaining Adversarial Attacks
In this work, we aim to explain the classifications of adversary images ...
research
∙
12/05/2018