We propose an end-to-end approach for gaze target detection: predicting a
head-target connection between individuals and the target image regions they
are looking at. Most of the existing methods use independent components such as
off-the-shelf head detectors or have problems in establishing associations
between heads and gaze targets. In contrast, we investigate an end-to-end
multi-person Gaze target detection framework with Heads and Targets Association
(GazeHTA), which predicts multiple head-target instances based solely on input
scene image. GazeHTA addresses challenges in gaze target detection by (1)
leveraging a pre-trained diffusion model to extract scene features for rich
semantic understanding, (2) re-injecting a head feature to enhance the head
priors for improved head understanding, and (3) learning a connection map as
the explicit visual associations between heads and gaze targets. Our extensive
experimental results demonstrate that GazeHTA outperforms state-of-the-art gaze
target detection methods and two adapted diffusion-based baselines on two
standard datasets.

我们提出了一种端到端的方法来检测凝视目标：预测个体和他们正在注视的目标图像区域之间的头目标连接。我们的实验结果表明，GazeHTA 在两个标准数据集上优于最先进的凝视目标检测方法和两个改进的基于扩散的基准模型。