In this paper we aim for zero-shot classification, that is visual recognition of an unseen class by using knowledge transfer from known classes. Our main contribution is COSTA, which exploits co-occurrences of visual concepts in images for knowledge transfer. These inter-dependencies arise naturally between concepts, and are easy to obtain from existing annotations or web-search hit counts. We estimate a classifier for a new label, as a weighted combination of related classes, using the co-occurrences to define the weight. We propose various metrics to leverage these cooccurrences, and a regression model for learning a weight for each related class. We also show that our zero-shot classifiers can serve as priors for few-shot learning. Experiments on three multi-labeled datasets reveal that our proposed zero-shot methods, are approaching and occasionally outperforming fully supervised SVMs. We conclude that cooccurrence statistics suffice for zero-shot classification.
REF develop an approach specifically targeting images that focuses on exploiting co-occurrences of visual concepts in images for knowledge transfer.
COSTA: Co-Occurrence Statistics for Zero-Shot Classification
2014 IEEE Conference on Computer Vision and Pattern Recognition
2014 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science