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Sketch-a-Classifier: Sketch-Based Photo Classifier Generation

Hu, C., Li, D., Song, Yi-Zhe, Xiang, T. and Hospedales, T.M. (2019) Sketch-a-Classifier: Sketch-Based Photo Classifier Generation In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-22 Jun 2018, Salt Lake City, Utah, USA.

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Abstract

Contemporary deep learning techniques have made image recognition a reasonably reliable technology. However training effective photo classifiers typically takes numerous examples which limits image recognition's scalability and applicability to scenarios where images may not be available. This has motivated investigation into zero-shot learning, which addresses the issue via knowledge transfer from other modalities such as text. In this paper we investigate an alternative approach of synthesizing image classifiers: Almost directly from a user's imagination, via freehand sketch. This approach doesn't require the category to be nameable or describable via attributes as per zero-shot learning. We achieve this via training a model regression network to map from free-hand sketch space to the space of photo classifiers. It turns out that this mapping can be learned in a category-agnostic way, allowing photo classifiers for new categories to be synthesized by user with no need for annotated training photos. We also demonstrate that this modality of classifier generation can also be used to enhance the granularity of an existing photo classifier, or as a complement to name-based zero-shot learning.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Hu, C.
Li, D.
Song, Yi-Zhey.song@surrey.ac.uk
Xiang, T.
Hospedales, T.M.
Date : February 2019
DOI : 10.1109/CVPR.2018.00952
Related URLs :
Additional Information : Printed proceedings available from Curran Associates Inc., ISBN 9781538664216.
Depositing User : Clive Harris
Date Deposited : 03 Jul 2019 10:10
Last Modified : 03 Jul 2019 10:10
URI: http://epubs.surrey.ac.uk/id/eprint/852105

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