RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open
  Environments
Mengxue Qu Yu Wu Wu Liu Xiaodan Liang Jingkuan Song Yao Zhao Yunchao Wei
Intention-oriented object detection aims to detect desired objects based on
specific intentions or requirements. For instance, when we desire to "lie down
and rest", we instinctively seek out a suitable option such as a "bed" or a
"sofa" that can fulfill our needs. Previous work in this area is limited either
by the number of intention descriptions or by the affordance vocabulary
available for intention objects. These limitations make it challenging to
handle intentions in open environments effectively. To facilitate this
research, we construct a comprehensive dataset called Reasoning
Intention-Oriented Objects (RIO). In particular, RIO is specifically designed
to incorporate diverse real-world scenarios and a wide range of object
categories. It offers the following key features: 1) intention descriptions in
RIO are represented as natural sentences rather than a mere word or verb
phrase, making them more practical and meaningful; 2) the intention
descriptions are contextually relevant to the scene, enabling a broader range
of potential functionalities associated with the objects; 3) the dataset
comprises a total of 40,214 images and 130,585 intention-object pairs. With the
proposed RIO, we evaluate the ability of some existing models to reason
intention-oriented objects in open environments.