  Zero-shot learning (ZSL) aims to recognize novel classes by transferring
semantic knowledge from seen classes to unseen classes. Since semantic
knowledge is built on attributes shared between different classes, which are
highly local, strong prior for localization of object attribute is beneficial
for visual-semantic embedding. Interestingly, when recognizing unseen images,
human would also automatically gaze at regions with certain semantic clue.
Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to
improve the discriminative attribute localization based on the class-level
attributes for ZSL. We aim to predict the actual human gaze location to get the
visual attention regions for recognizing a novel object guided by attribute
description. Specifically, the task-dependent attention is learned with the
goal-oriented GEM, and the global image features are simultaneously optimized
with the regression of local attribute features. Experiments on three ZSL
benchmarks, i.e., CUB, SUN and AWA2, show the superiority or competitiveness of
our proposed method against the state-of-the-art ZSL methods. The ablation
analysis on real gaze data CUB-VWSW also validates the benefits and accuracy of
our gaze estimation module. This work implies the promising benefits of
collecting human gaze dataset and automatic gaze estimation algorithms on
high-level computer vision tasks. The code is available at
https://github.com/osierboy/GEM-ZSL.
