Zhang C, Lin G, Liu F, Yao R, Shen C (2019) CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. Liu Y, Zhang X, Zhang S, He X (2020) Part-aware prototype network for few-shot semantic segmentation. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Li G, Jampani V, Sevilla-Lara L, Sun D, Kim J, Kim J (2021) Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9197–9206 Wang K, Liew J.H, Zou Y, Zhou D, Feng J (2019) Panet: few-shot image semantic segmentation with prototype alignment. One-shot learning for semantic segmentation. Shaban A, Bansal S, Liu Z, Essa I, Boots B (2017). In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213–3223. Ĭordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding.

Įveringham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Experimental results on three benchmark datasets demonstrate that the proposed method surpasses some previous state-of-the-art approaches well, especially performing favorably in handling challenging situations under 1-shot setting. We further introduce the prior affinity map to impart inductive bias and eliminate interfering semantics. Specifically, we adaptively aggregate support and query features by a non-local bidirectional information flow in a hierarchical manner to derive a closer and deeper correlation. We propose a hierarchical bidirectional aggregation with prior guided transformer for abundant intra-class common cues. Despite demonstrated successes, some existing methods might suffer from the intra-class inconsistency between query and support samples for local unidirectional information guidance. Recent years have witnessed significant interest in few-shot segmentation methods, with the aim of predicting novel categories in a query image given the limited labeled support set.

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