We propose HOI Transformer to tackle human object interaction (HOI) detection
in an end-to-end manner. Current approaches either decouple HOI task into
separated stages of object detection and interaction classification or
introduce surrogate interaction problem. In contrast, our method, named HOI
Transformer, streamlines the HOI pipeline by eliminating the need for many
hand-designed components. HOI Transformer reasons about the relations of
objects and humans from global image context and directly predicts HOI
instances in parallel. A quintuple matching loss is introduced to force HOI
predictions in a unified way. Our method is conceptually much simpler and
demonstrates improved accuracy. Without bells and whistles, HOI Transformer
achieves $26.61\% $ $ AP $ on HICO-DET and $52.9\%$ $AP_{role}$ on V-COCO,
surpassing previous methods with the advantage of being much simpler. We hope
our approach will serve as a simple and effective alternative for HOI tasks.
Code is available at this https URL .

HOI Transformer 是一种用于解决人与物体交互检测任务的算法，它可以通过全局图像上下文来推断物体与人的关系并在并行统一的方式下直接预测 HOI 实例，并引入了五元匹配损失来统一强制 HOI 预测，并能够在 HICO-DET 和 V-COCO 上取得比以前更好的性能表现。

HOI Transformer 实现端到端人体对象交互检测

End-to-End Human Object Interaction Detection with HOI Transformer

We propose a novel method for detecting horizontal vanishing points and the
zenith vanishing point in man-made environments. The dominant trend in existing
methods is to first find candidate vanishing points, then remove outliers by
enforcing mutual orthogonality. Our method reverses this process: we propose a
set of horizon line candidates and score each based on the vanishing points it
contains. A key element of our approach is the use of global image context,
extracted with a deep convolutional network, to constrain the set of candidates
under consideration. Our method does not make a Manhattan-world assumption and
can operate effectively on scenes with only a single horizontal vanishing
point. We evaluate our approach on three benchmark datasets and achieve
state-of-the-art performance on each. In addition, our approach is
significantly faster than the previous best method.

本文提出了一种在人工环境中检测水平消失点和天顶消失点的新方法，利用深度卷积网络提取全局图像上下文，并逆转现有方法的先后顺序，通过提出一组地平线候选者并针对其包含的消失点对其进行打分，而非先找到候选消失点再通过强制互相正交来消除异常值。我们在三个基准数据集上评估了我们的方法，并在每个数据集上均取得了最好的性能，同时我们的方法明显快于之前最好的方法。