37 citations · 40 across the 4 of their papers we have counts for
5 papers · 1 filter
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
Lin Li, Haoyan Guan, Jianing Qiu +1
Large pre-trained Vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the ad…
The Importance of Anti-Aliasing in Tiny Object Detection
Jinlai Ning, Michael Spratling
Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. Howeve…
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation
Lin Li, Jianing Qiu, Michael Spratling
Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is an effective defense against adversarial examples. However, AT is prone to overfitting whi…
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing
Lin Li, Michael Spratling
Deep neural networks can be easily fooled into making incorrect predictions through corruption of the input by adversarial perturbations: human-imperceptible artificial noise. So f…
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation aims to segment images containing objects from previously unseen classes using only a few annotated samples. Most current methods focus on using object inform…