  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.
