Feature Weighting and Boosting for Few-Shot Segmentation

Khoi Nguyen, Sinisa Todorovic; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 622-631

Abstract


This paper is about few-shot segmentation of foreground objects in images. We train a CNN on small subsets of training images, each mimicking the few-shot setting. In each subset, one image serves as the query and the other(s) as support image(s) with ground-truth segmentation. The CNN first extracts feature maps from the query and support images. Then, a class feature vector is computed as an average of the support's feature maps over the known foreground. Finally, the target object is segmented in the query image by using a cosine similarity between the class feature vector and the query's feature map. We make two contributions by: (1) Improving discriminativeness of features so their activations are high on the foreground and low elsewhere; and (2) Boosting inference with an ensemble of experts guided with the gradient of loss incurred when segmenting the support images in testing. Our evaluations on the PASCAL-5i and COCO-20i datasets demonstrate that we significantly outperform existing approaches.

Related Material


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[bibtex]
@InProceedings{Nguyen_2019_ICCV,
author = {Nguyen, Khoi and Todorovic, Sinisa},
title = {Feature Weighting and Boosting for Few-Shot Segmentation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}