Spatial relationships between objects represent key scene information for
humans to understand and interact with the world. To study the capability of
current computer vision systems to recognize physically grounded spatial
relations, we start by proposing precise relation definitions that permit
consistently annotating a benchmark dataset. Despite the apparent simplicity of
this task relative to others in the recognition literature, we observe that
existing approaches perform poorly on this benchmark. We propose new approaches
exploiting the long-range attention capabilities of transformers for this task,
and evaluating key design principles. We identify a simple "RelatiViT"
architecture and demonstrate that it outperforms all current approaches. To our
knowledge, this is the first method to convincingly outperform naive baselines
on spatial relation prediction in in-the-wild settings. The code and datasets
are available in https://sites.google.com/view/spatial-relation.

当前计算机视觉系统在识别实物基础空间关系方面的性能较差，通过提出了精确的关系定义以允许对基准数据集进行一致的标注，并利用 Transformer 模型的长程注意力能力对这一任务提出新的方法进行评估。我们提出了一种名为 “RelatiViT” 的简单架构，并证明其胜过所有当前方法，这是第一种在实际场景中令人信服地优于朴素基准的空间关系预测方法。