12 citations · 17 across the 6 of their papers we have counts for
6 papers
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning
Chaoqin Huang, Haoyan Guan, Aofan Jiang +4
Most existing anomaly detection (AD) methods require a dedicated model for each category. Such a paradigm, despite its promising results, is computationally expensive and inefficie…
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…
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…
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…
Query Semantic Reconstruction for Background in Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated suppo…
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…