Deep Active Object Recognition by Joint Label and Action Prediction
  An active object recognition system has the advantage of being able to act in
the environment to capture images that are more suited for training and that
lead to better performance at test time. In this paper, we propose a deep
convolutional neural network for active object recognition that simultaneously
predicts the object label, and selects the next action to perform on the object
with the aim of improving recognition performance. We treat active object
recognition as a reinforcement learning problem and derive the cost function to
train the network for joint prediction of the object label and the action. A
generative model of object similarities based on the Dirichlet distribution is
proposed and embedded in the network for encoding the state of the system. The
training is carried out by simultaneously minimizing the label and action
prediction errors using gradient descent. We empirically show that the proposed
network is able to predict both the object label and the actions on GERMS, a
dataset for active object recognition. We compare the test label prediction
accuracy of the proposed model with Dirichlet and Naive Bayes state encoding.
The results of experiments suggest that the proposed model equipped with
Dirichlet state encoding is superior in performance, and selects images that
lead to better training and higher accuracy of label prediction at test time.
