  We propose a novel recurrent attentional structure to localize and recognize
objects jointly. The network can learn to extract a sequence of local
observations with detailed appearance and rough context, instead of sliding
windows or convolutions on the entire image. Meanwhile, those observations are
fused to complete detection and classification tasks. On training, we present a
hybrid loss function to learn the parameters of the multi-task network
end-to-end. Particularly, the combination of stochastic and object-awareness
strategy, named SA, can select more abundant context and ensure the last
fixation close to the object. In addition, we build a real-world dataset to
verify the capacity of our method in detecting the object of interest including
those small ones. Our method can predict a precise bounding box on an image,
and achieve high speed on large images without pooling operations. Experimental
results indicate that the proposed method can mine effective context by several
local observations. Moreover, the precision and speed are easily improved by
changing the number of recurrent steps. Finally, we will open the source code
of our proposed approach.
