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Most hard attention models initially observe a complete scene to locate andsense informative glimpses, and predict class-label of a scene based onglimpses. However, in many applications (e.g., aerial imaging), observing anentire scene is not always feasible due to the limited time and resourcesavailable for acquisition. In this paper, we develop a Sequential TransformersAttention Model (STAM) that only partially observes a complete image andpredicts informative glimpse locations solely based on past glimpses. We designour agent using DeiT-distilled and train it with a one-step actor-criticalgorithm. Furthermore, to improve classification performance, we introduce anovel training objective, which enforces consistency between the classdistribution predicted by a teacher model from a complete image and the classdistribution predicted by our agent using glimpses. When the agent senses only4% of the total image area, the inclusion of the proposed consistency loss inour training objective yields 3% and 8% higher accuracy on ImageNet and fMoWdatasets, respectively. Moreover, our agent outperforms previousstate-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses onImageNet and fMoW.

Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes