202 citations · 690 across the 19 of their papers we have counts for
6 papers · 2 filters
DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation
Shengcong Chen, Changxing Ding, Dacheng Tao +1
Nucleus segmentation is usually the first step in pathological image analysis tasks. Generalizable nucleus segmentation refers to the problem of training a segmentation model that…
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
Muzhi Zhu, Hengtao Li, Hao Chen +5
Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel object…
Dense Affinity Matching for Few-Shot Segmentation
Hao Chen, Yonghan Dong, Zheming Lu +4
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the…
Low-Light Enhancement in the Frequency Domain
Hao Chen, Zhi Jin
Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. These visual disturbances further reduce the performance of high-level…
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
Jia-Xin Zhuang, Luyang Luo, Hao Chen
Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However,…
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Jintao Rong, Hao Chen, Linlin Ou +3
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment i…