most citedFECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network

105 citations · 174 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Holistic Prototype Attention Network for Few-Shot VOS

Yin Tang, Tao Chen, Xiruo Jiang +3

Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotatio…

cs.CV202345 cited

Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation

Tao Chen, Yazhou Yao, Jinhui Tang

Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts. Howe…

cs.CV2023

Semi-Supervised Semantic Segmentation With Region Relevance

Rui Chen, Tao Chen, Qiong Wang +1

Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to gener…

cs.CV202320 cited

Co-attention Propagation Network for Zero-Shot Video Object Segmentation

Gensheng Pei, Yazhou Yao, Fumin Shen +3

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often…

cs.CV20232 cited

Attention Map Guided Transformer Pruning for Edge Device

Junzhu Mao, Yazhou Yao, Zeren Sun +3

Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identificatio…

cs.CV2023105 cited

FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network

Huafeng Liu, Pai Peng, Tao Chen +3

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images. The current correlation-…