Iterative learning control

Control (management)

Linguistics

Engineering

Philosophy

Pascal (unit)

Aerospace engineering

Computer science

Scale-space segmentation

Image segmentation

Mechanical engineering

Organic chemistry

Iterative refinement

Segmentation

Pattern recognition (psychology)

Feature (linguistics)

Artificial intelligence

Chemistry

Programming language

Deep learning

Intersection (aeronautics)

Shot (pellet)

Margin (machine learning)

Class (philosophy)

Convolutional neural network

Machine learning

One shot

Computer vision

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning