  Being able to predict human gaze behavior has obvious importance for
behavioral vision and for computer vision applications. Most models have mainly
focused on predicting free-viewing behavior using saliency maps, but these
predictions do not generalize to goal-directed behavior, such as when a person
searches for a visual target object. We propose the first inverse reinforcement
learning (IRL) model to learn the internal reward function and policy used by
humans during visual search. The viewer's internal belief states were modeled
as dynamic contextual belief maps of object locations. These maps were learned
by IRL and then used to predict behavioral scanpaths for multiple target
categories. To train and evaluate our IRL model we created COCO-Search18, which
is now the largest dataset of high-quality search fixations in existence.
COCO-Search18 has 10 participants searching for each of 18 target-object
categories in 6202 images, making about 300,000 goal-directed fixations. When
trained and evaluated on COCO-Search18, the IRL model outperformed baseline
models in predicting search fixation scanpaths, both in terms of similarity to
human search behavior and search efficiency. Finally, reward maps recovered by
the IRL model reveal distinctive target-dependent patterns of object
prioritization, which we interpret as a learned object context.
