2 citations · 2 across the 4 of their papers we have counts for
6 papers
Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings
Chao Qin, Fahad Shahbaz Khan, Salman Khan +4
Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when device, operator, or anatomy cha…
iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers hav…
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…
Multi-Granularity Language-Guided Training for Multi-Object Tracking
Yuhao Li, Jiale Cao, Muzammal Naseer +4
Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same…
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
Bin Xie, Jiale Cao, Jin Xie +2
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-train…
Remote Sensing Change Detection With Transformers Trained from Scratch
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +4
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-train…