  Traditional object detection answers two questions; "what" (what the object
is?) and "where" (where the object is?). "what" part of the object detection
can be fine-grained further i.e. "what type", "what shape" and "what material"
etc. This results in the shifting of the object detection tasks to the object
description paradigm. Describing an object provides additional detail that
enables us to understand the characteristics and attributes of the object
("plastic boat" not just boat, "glass bottle" not just bottle). This additional
information can implicitly be used to gain insight into unseen objects (e.g.
unknown object is "metallic", "has wheels"), which is not possible in
traditional object detection. In this paper, we present a new approach to
simultaneously detect objects and infer their attributes, we call it Detect and
Describe (DaD) framework. DaD is a deep learning-based approach that extends
object detection to object attribute prediction as well. We train our model on
aPascal train set and evaluate our approach on aPascal test set. We achieve
97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for
object attributes prediction on aPascal test set. We also show qualitative
results for object attribute prediction on unseen objects, which demonstrate
the effectiveness of our approach for describing unknown objects.
