  Active visual exploration addresses the issue of limited sensor capabilities
in real-world scenarios, where successive observations are actively chosen
based on the environment. To tackle this problem, we introduce a new technique
called Attention-Map Entropy (AME). It leverages the internal uncertainty of
the transformer-based model to determine the most informative observations. In
contrast to existing solutions, it does not require additional loss components,
which simplifies the training. Through experiments, which also mimic
retina-like sensors, we show that such simplified training significantly
improves the performance of reconstruction, segmentation and classification on
publicly available datasets.
