Hypercorrelation Squeeze for Few-Shot Segmentation

• URL: http://arxiv.org/abs/2104.01538v1
• Date: Sun, 4 Apr 2021 05:27:13 GMT
• Title: Hypercorrelation Squeeze for Few-Shot Segmentation
• Authors: Juhong Min, Dahyun Kang, Minsu Cho
• Abstract summary: Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenge requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. We propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions.
• Score: 33.45698767126036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. To address the problem, we propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions. It extracts diverse features from different levels of intermediate convolutional layers and constructs a collection of 4D correlation tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions in a pyramidal architecture, the method gradually squeezes high-level semantic and low-level geometric cues of the hypercorrelation into precise segmentation masks in coarse-to-fine manner. The significant performance improvements on standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000 verify the efficacy of the proposed method.

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