Hypercorrelation Squeeze for Few-Shot Segmentation
arXiv:2104.01538
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.
arXiv commentAccepted to ICCV 2021
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- Cross Attention Network for Few-shot Classification
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Cited by in corpus (5)
- FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
- Few-Shot Segmentation via Cycle-Consistent Transformer
- Iterative Few-shot Semantic Segmentation from Image Label Text
- CATs: Cost Aggregation Transformers for Visual Correspondence
- Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence