Gaze Embeddings for Zero-Shot Image Classification
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2016
- DOI:
- 10.48550/arXiv.1611.09309
- arXiv:
- arXiv:1611.09309
- Bibcode:
- 2016arXiv161109309K
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition