Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning.

ABSTRACT: Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but 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. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned 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-Searc

SUBMITTER: Yang Z 

PROVIDER: S-EPMC8218821 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature