  Objects of different classes can be described using a limited number of
attributes such as color, shape, pattern, and texture. Learning to detect
object attributes instead of only detecting objects can be helpful in dealing
with a priori unknown objects. With this inspiration, a deep convolutional
neural network for low-level object attribute classification, called the Deep
Attribute Network (DAN), is proposed. Since object features are implicitly
learned by object recognition networks, one such existing network is modified
and fine-tuned for developing DAN. The performance of DAN is evaluated on the
ImageNet Attribute and a-Pascal datasets. Experiments show that in comparison
with state-of-the-art methods, the proposed model achieves better results.
