Joint Gaze-Location and Gaze-Object Detection
Danyang Tu Wei Shen Wei Sun Xiongkuo Min Guangtao Zhai
This paper proposes an efficient and effective method for joint gaze location
detection (GL-D) and gaze object detection (GO-D), \emph{i.e.}, gaze following
detection. Current approaches frame GL-D and GO-D as two separate tasks,
employing a multi-stage framework where human head crops must first be detected
and then be fed into a subsequent GL-D sub-network, which is further followed
by an additional object detector for GO-D. In contrast, we reframe the gaze
following detection task as detecting human head locations and their gaze
followings simultaneously, aiming at jointly detect human gaze location and
gaze object in a unified and single-stage pipeline. To this end, we propose
GTR, short for \underline{G}aze following detection \underline{TR}ansformer,
streamlining the gaze following detection pipeline by eliminating all
additional components, leading to the first unified paradigm that unites GL-D
and GO-D in a fully end-to-end manner. GTR enables an iterative interaction
between holistic semantics and human head features through a hierarchical
structure, inferring the relations of salient objects and human gaze from the
global image context and resulting in an impressive accuracy. Concretely, GTR
achieves a 12.1 mAP gain ($\mathbf{25.1}\%$) on GazeFollowing and a 18.2 mAP
gain ($\mathbf{43.3\%}$) on VideoAttentionTarget for GL-D, as well as a 19 mAP
improvement ($\mathbf{45.2\%}$) on GOO-Real for GO-D. Meanwhile, unlike
existing systems detecting gaze following sequentially due to the need for a
human head as input, GTR has the flexibility to comprehend any number of
people's gaze followings simultaneously, resulting in high efficiency.
Specifically, GTR introduces over a $\times 9$ improvement in FPS and the
relative gap becomes more pronounced as the human number grows.