Gaze Embeddings for Zero-Shot Image Classification
Nour Karessli, Zeynep Akata, Bernt Schiele, Andreas Bulling
Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6412-6421, 2017.
Spotlight presentation
Abstract
Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership. We present a data collection paradigm that involves a discrimination task to increase the information content obtained from gaze data. Our method extracts discriminative descriptors from the data and learns a compatibility function between image and gaze using three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid (GFG) and Gaze Features with Sequence (GFS). We introduce two new gaze-annotated datasets for fine-grained image classification and show that human gaze data is indeed class discriminative, provides a competitive alternative to expert-annotated attributes, and outperforms other baselines for zero-shot image classification.Links
BibTeX
@inproceedings{karessli17_cvpr,
title = {Gaze Embeddings for Zero-Shot Image Classification},
author = {Karessli, Nour and Akata, Zeynep and Schiele, Bernt and Bulling, Andreas},
year = {2017},
booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {6412-6421},
doi = {10.1109/CVPR.2017.679}
}