Prompt engineering is a technique that involves augmenting a large
pre-t...
Large language models (LLMs) demonstrate remarkable medical expertise, b...
With the advent of vision-language models (VLMs) that can perform in-con...
The slowing down of Moore's law has driven the development of unconventi...
Deep learning-based change detection using remote sensing images has rec...
“Effective robustness” measures the extra out-of-distribution (OOD)
robu...
Large pre-trained language models have shown remarkable performance over...
The recent advances in Vision Transformer (ViT) have demonstrated its
im...
We investigate the robustness of vision transformers (ViTs) through the ...
Change detection (CD) in remote sensing images has been an ever-expandin...
NLP models are shown to suffer from robustness issues, i.e., a model's
p...
There has been an ongoing cycle where stronger defenses against adversar...
Adversarial examples raise questions about whether neural network models...
Adversarial examples are inputs to machine learning models designed by a...
Existing studies about ambient backscatter communication mostly assume
f...
This paper presents a tensor alignment (TA) based domain adaptation meth...
This paper introduces a novel heterogenous domain adaptation (HDA) metho...
We propose the autofocus convolutional layer for semantic segmentation w...
Saliency detection, finding the most important parts of an image, has be...
The Nonlinear autoregressive exogenous (NARX) model, which predicts the
...