A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 4; pp. 1 - 13
• Main Authors: Gao, Honghao, Xiao, Junsheng, Yin, Yuyu, Liu, Tong, Shi, Jiangang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Computer vision; Convolution; Datasets; Feature maps; Few-shot learning; graph attention network; graph reasoning; Graph theory; Image edge detection; Image enhancement; Image processing; Image segmentation; mutually supervised regime (MSR); Neural networks; Pixels; Queries; Semantic segmentation; Semantics; Spatial data; Task analysis; Training

Fully supervised semantic segmentation has performed well in many computer vision tasks. However, it is time-consuming because training a model requires a large number of pixel-level annotated samples. Few-shot segmentation has recently become a popular approach to addressing this problem, as it req...