18 citations · 19 across the 3 of their papers we have counts for
4 papers
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
Xuhai Chen, Jiangning Zhang, Guanzhong Tian +5
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects. We propose a framework called CLIP-AD to leverage the zero-shot c…
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Liang Liu, Boshen Zhang, Jiangning Zhang +6
Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particu…
Calibrated Teacher for Sparsely Annotated Object Detection
Haohan Wang, Liang Liu, Boshen Zhang +6
Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidab…
Iterative Few-shot Semantic Segmentation from Image Label Text
Haohan Wang, Liang Liu, Wuhao Zhang +5
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of…