Hypercorrelation Squeeze for Few-Shot Segmentation
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.
Code (1)
Tasks
Feature CorrelationFew-Shot Semantic SegmentationSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hypercorrelation Squeeze for Few-Shot Segmenation
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…
Feature CorrelationFew-Shot Semantic SegmentationSegmentationSemantic SegmentationCross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train generalized models that can segment classes from different domains with a few labeled images. Previous works have proven the effectiveness of feature tra…
Cross-Domain Few-ShotFew-Shot Semantic SegmentationSemantic SegmentationTarget-aware Bi-Transformer for Few-shot Segmentation
Traditional semantic segmentation tasks require a large number of labels and are difficult to identify unlearned categories. Few-shot semantic segmentation (FSS) aims to use limited labeled support images to identify the…
Few-Shot Semantic SegmentationSegmentationSemantic Segmentation'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class fro…
Few-Shot LearningImage SegmentationOrgan SegmentationSegmentation+1Integrative Few-Shot Learning for Classification and Segmentation
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This tas…
ClassificationFew-Shot Classification and SegmentationFew-Shot LearningFew-Shot Semantic Segmentation+3