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Gaze Embeddings for Zero-Shot Image Classification

Nour Karessli, Zeynep Akata, Bernt Schiele, Andreas Bulling

arXiv:1611.09309, pp. 1–10, 2016.


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.



@techreport{karessli16_arxiv, title = {Gaze Embeddings for Zero-Shot Image Classification}, author = {Karessli, Nour and Akata, Zeynep and Schiele, Bernt and Bulling, Andreas}, year = {2016}, pages = {1--10}, url = {} }