62 citations · 110 across the 9 of their papers we have counts for
7 papers · 1 filter
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Yanyu Ye, Zhenxi Zhang, Wei Wei +1
Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target dom…
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving compar…
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abund…
The CLIP Model is Secretly an Image-to-Prompt Converter
Yuxuan Ding, Chunna Tian, Haoxuan Ding +1
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Traini…
Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
Heng Zhou, Chunna Tian, Zhenxi Zhang +4
RGB-Thermal salient object detection (SOD) combines two spectra to segment visually conspicuous regions in images. Most existing methods use boundary maps to learn the sharp bounda…
Don't Stop Learning: Towards Continual Learning for the CLIP Model
Yuxuan Ding, Lingqiao Liu, Chunna Tian +2
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Be…
PixelGame: Infrared small target segmentation as a Nash equilibrium
Heng Zhou, Chunna Tian, Zhenxi Zhang +3
A key challenge of infrared small target segmentation (ISTS) is to balance false negative pixels (FNs) and false positive pixels (FPs). Traditional methods combine FNs and FPs into…