  Active Visual Exploration (AVE) is a task that involves dynamically selecting
observations (glimpses), which is critical to facilitate comprehension and
navigation within an environment. While modern AVE methods have demonstrated
impressive performance, they are constrained to fixed-scale glimpses from rigid
grids. In contrast, existing mobile platforms equipped with optical zoom
capabilities can capture glimpses of arbitrary positions and scales. To address
this gap between software and hardware capabilities, we introduce AdaGlimpse.
It uses Soft Actor-Critic, a reinforcement learning algorithm tailored for
exploration tasks, to select glimpses of arbitrary position and scale. This
approach enables our model to rapidly establish a general awareness of the
environment before zooming in for detailed analysis. Experimental results
demonstrate that AdaGlimpse surpasses previous methods across various visual
tasks while maintaining greater applicability in realistic AVE scenarios.
