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

TL;DR

This paper introduces the Detect-and-Describe (DaD) framework, a deep learning model that jointly detects objects and predicts their attributes, enhancing understanding of both known and unseen objects.

Contribution

The paper presents a novel joint detection and attribute prediction framework that extends traditional object detection to include detailed object descriptions.

Findings

Achieved 97.0% AUC for attribute prediction on aPascal test set.

Demonstrated effectiveness in describing unseen objects.

Extended object detection to include attribute inference.

Abstract

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.