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.
翻译:传统天体探测回答两个问题 : “ 是什么 ” (对象是什么? ) 和 “ 哪里 ” (对象在哪里? ) 。 “ 物体探测的哪些部分” 可以进一步细化, 即“ 哪种类型”、“ 形状” 和“ 物质” 等。 这导致天体探测任务向对象描述范式转移。 描述一个对象提供了更多的细节, 使我们能够理解对象的特性和属性( “ 塑料船”, 不仅仅是船, “玻璃瓶” ) 和“ 哪里 ” (对象在哪里? ) 。 这种额外信息可以隐含地用来了解不可见的物体( 例如, 未知物体是“ 金属 ”, “ 轮子” ), 在传统天体探测中是不可能的。 在本文中, 我们提出了一个同时探测对象并推断其属性的新方法, 我们称之为“ 检测和描述( DaD) 框架。 DADAD是一个深层次的基于学习的方法, 将天体探测对象检测扩展到对象的属性预测。 我们用一个模型来评估一个Pascal 测试对象的设置 。 我们在“ ” 区域实现97.%, 在“ ” 操作特性预测 中, 显示一个未知的特性目标的特性分析仪上显示一个未知的特性 。