Creativity Inspired Zero-Shot Learning

Mohamed Elhoseiny, Mohamed Elfeki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

49 Scopus citations

Abstract

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of zero-shot learning, we model the visual learning process of unseen categories with an inspiration from the psychology of human creativity for producing novel art. We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Empirically, we show consistent improvement over the state of the art of several percents on the largest available benchmarks on the challenging task or generalized ZSL from a noisy text that we focus on, using the CUB and NABirds datasets. We also show the advantage of our loss on Attribute-based ZSL on three additional datasets (AwA2, aPY, and SUN). Code is available at https://github.com/mhelhoseiny/CIZSL.
Original languageEnglish (US)
Title of host publication2019 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE
Pages5783-5792
Number of pages10
ISBN (Print)9781728148038
DOIs
StatePublished - 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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