论文标题
检测和描述：检测和描述对象的联合学习框架
Detect-and-describe: Joint learning framework for detection and description of objects
论文作者
论文摘要
传统对象检测回答了两个问题； “什么”（对象是什么？）和“在哪里”（对象在哪里？）。对象检测的“什么”一部分可以进一步细粒，即“哪种类型”，“什么形状”和“什么材料”等。这导致对象检测任务转移到对象描述范式上。描述一个物体提供了其他细节，使我们能够了解物体的特征和属性（“塑料船”不仅是船，“玻璃瓶”，而不仅仅是瓶子）。可以隐式地使用此其他信息来洞悉看不见的对象（例如未知对象是“金属”，“有车轮”），这在传统对象检测中是不可能的。在本文中，我们提出了一种同时检测对象并推断其属性的新方法，我们称其为检测和描述（爸爸）框架。 DAD是一种基于深度学习的方法，它也将对象检测扩展到对象属性预测。我们在Apascal Train设置上训练模型，并评估我们在Apascal测试集上的方法。我们在接收器操作特征曲线（AUC）下的面积达到97.0％，以实现对象属性的预测。我们还显示了对对象属性预测对象对象的定性结果，这些结果证明了我们描述未知对象的方法的有效性。
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.
