In this paper, we propose an effective and efficient method for
Human-Gaze-Target (HGT) detection, i.e., gaze following. Current approaches
decouple the HGT detection task into separate branches of salient object
detection and human gaze prediction, employing a two-stage framework where
human head locations must first be detected and then be fed into the next gaze
target prediction sub-network. In contrast, we redefine the HGT detection task
as detecting human head locations and their gaze targets, simultaneously. By
this way, our method, named Human-Gaze-Target detection TRansformer or HGTTR,
streamlines the HGT detection pipeline by eliminating all other additional
components. HGTTR reasons about the relations of salient objects and human gaze
from the global image context. Moreover, unlike existing two-stage methods that
require human head locations as input and can predict only one human's gaze
target at a time, HGTTR can directly predict the locations of all people and
their gaze targets at one time in an end-to-end manner. The effectiveness and
robustness of our proposed method are verified with extensive experiments on
the two standard benchmark datasets, GazeFollowing and VideoAttentionTarget.
Without bells and whistles, HGTTR outperforms existing state-of-the-art methods
by large margins (6.4 mAP gain on GazeFollowing and 10.3 mAP gain on
VideoAttentionTarget) with a much simpler architecture.

我们提出了一种名为 HGTTR 的方法，它可以在同时检测人类头部位置和目标注视物，在全局图像背景下推导关于显著对象与人眼注视之间的关系，这种方法不仅比现有的基于两阶段方法在性能上更好，而且可以在端到端的方式下直接预测所有人的头部位置和目标注视物。