9 citations · 18 across the 7 of their papers we have counts for
7 papers
DB-SAM: Delving into High Quality Universal Medical Image Segmentation
Chao Qin, Jiale Cao, Huazhu Fu +2
Recently, the Segment Anything Model (SAM) has demonstrated promising segmentation capabilities in a variety of downstream segmentation tasks. However in the context of universal m…
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Feng Yan, Xiaoheng Jiang, Yang Lu +5
Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection…
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Xiaoheng Jiang, Kaiyi Guo, Yang Lu +5
Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress,…
A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos
Chao Qin, Jiale Cao, Huazhu Fu +2
Detecting breast lesion in videos is crucial for computer-aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of…
DFormer: Diffusion-guided Transformer for Universal Image Segmentation
Hefeng Wang, Jiale Cao, Rao Muhammad Anwer +3
This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diff…
LEAPS: End-to-End One-Step Person Search With Learnable Proposals
Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer +3
We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search…