  Active visual exploration aims to assist an agent with a limited field of
view to understand its environment based on partial observations made by
choosing the best viewing directions in the scene. Recent methods have tried to
address this problem either by using reinforcement learning, which is difficult
to train, or by uncertainty maps, which are task-specific and can only be
implemented for dense prediction tasks. In this paper, we propose the
Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the
visual exploration instead of task-specific uncertainty maps; (b) can be used
for both dense and sparse prediction tasks; and (c) uses a contrastive stream
to further improve the representations learned. Unlike previous works, we show
the application of our model on multiple tasks like reconstruction,
segmentation and classification. Our model provides encouraging results while
being less dependent on dataset bias in driving the exploration. We further
perform an ablation study to investigate the features and attention learned by
our model. Finally, we show that our self-attention module learns to attend
different regions of the scene by minimizing the loss on the downstream task.
Code: https://github.com/soroushseifi/glimpse-attend-explore.
