Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU.

在本论文中，我们提出了基于一种二步检测器——Unary-Pairwise Transformer，它利用HOIs的一元和二元表示，通过使用与DETR相同的Transformer来训练，我们发现在相同的Transformer下，使用二步检测器比使用一步检测器可以提高预测性能和减少训练时间。我们在HICO-DET和V-COCO数据集上进行了评估，并明显优于最先进的方法。在推理时间内，我们的模型通过使用ResNet50能够在单个GPU上实现实时性能。

一种高效的两阶段人-物交互检测方法： 一种新型的一元-双元转换器

一种高效的两阶段人-物交互检测方法：一种新型的一元-双元转换器