4 citations · 7 across the 2 of their papers we have counts for
7 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…
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…
Rethinking the backbone architecture for tiny object detection
Jinlai Ning, Haoyan Guan, Michael Spratling
Tiny object detection has become an active area of research because images with tiny targets are common in several important real-world scenarios. However, existing tiny object det…
Data Augmentation Alone Can Improve Adversarial Training
Lin Li, Michael Spratling
Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. Data augmentation, which is effective at preventing overf…
Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang +3
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided…