  We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) - an
improved attention-based architecture for multiple object recognition. The
proposed model is a fully differentiable unit that can be optimized end-to-end
by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was
employed as visual attention mechanism which allows to learn the geometric
transformation of objects within images. With the combination of the Spatial
Transformer and the powerful recurrent architecture, the proposed EDRAM can
localize and recognize objects simultaneously. EDRAM has been evaluated on two
publicly available datasets including MNIST Cluttered (with 70K cluttered
digits) and SVHN (with up to 250k real world images of house numbers).
Experiments show that it obtains superior performance as compared with the
state-of-the-art models.
