A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning

Shafin Rahman*, Salman Khan, Fatih Porikli

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    132 Citations (Scopus)

    Abstract

    Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning problems. Our approach is based on a novel class adapting principal directions' (CAPDs) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for the selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen-unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot, and few/one-shot learning problems.

    Original languageEnglish
    Article number8423721
    Pages (from-to)5652-5667
    Number of pages16
    JournalIEEE Transactions on Image Processing
    Volume27
    Issue number11
    DOIs
    Publication statusPublished - Nov 2018

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