  Comprehending how humans process visual information in dynamic settings is
crucial for psychology and designing user-centered interactions. While mobile
eye-tracking systems combining egocentric video and gaze signals can offer
valuable insights, manual analysis of these recordings is time-intensive. In
this work, we present a novel human-centered learning algorithm designed for
automated object recognition within mobile eye-tracking settings. Our approach
seamlessly integrates an object detector with a spatial relation-aware
inductive message-passing network (I-MPN), harnessing node profile information
and capturing object correlations. Such mechanisms enable us to learn embedding
functions capable of generalizing to new object angle views, facilitating rapid
adaptation and efficient reasoning in dynamic contexts as users navigate their
environment. Through experiments conducted on three distinct video sequences,
our interactive-based method showcases significant performance improvements
over fixed training/testing algorithms, even when trained on considerably
smaller annotated samples collected through user feedback. Furthermore, we
demonstrate exceptional efficiency in data annotation processes and surpass
prior interactive methods that use complete object detectors, combine detectors
with convolutional networks, or employ interactive video segmentation.
