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Traditional object detection answers two questions; "what" (what the objectis?) and "where" (where the object is?). "what" part of the object detectioncan 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 objectdescription paradigm. Describing an object provides additional detail thatenables us to understand the characteristics and attributes of the object("plastic boat" not just boat, "glass bottle" not just bottle). This additionalinformation can implicitly be used to gain insight into unseen objects (e.g.unknown object is "metallic", "has wheels"), which is not possible intraditional object detection. In this paper, we present a new approach tosimultaneously detect objects and infer their attributes, we call it Detect andDescribe (DaD) framework. DaD is a deep learning-based approach that extendsobject detection to object attribute prediction as well. We train our model onaPascal train set and evaluate our approach on aPascal test set. We achieve97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) forobject attributes prediction on aPascal test set. We also show qualitativeresults for object attribute prediction on unseen objects, which demonstratethe effectiveness of our approach for describing unknown objects.

Detect-and-describe: Joint learning framework for detection and description of objects