  Egocentric perception has grown rapidly with the advent of immersive
computing devices. Human gaze prediction is an important problem in analyzing
egocentric videos and has primarily been tackled through either saliency-based
modeling or highly supervised learning. We quantitatively analyze the
generalization capabilities of supervised, deep learning models on the
egocentric gaze prediction task on unseen, out-of-domain data. We find that
their performance is highly dependent on the training data and is restricted to
the domains specified in the training annotations. In this work, we tackle the
problem of jointly predicting human gaze points and temporal segmentation of
egocentric videos without using any training data. We introduce an unsupervised
computational model that draws inspiration from cognitive psychology models of
event perception. We use Grenander's pattern theory formalism to represent
spatial-temporal features and model surprise as a mechanism to predict gaze
fixation points. Extensive evaluation on two publicly available datasets - GTEA
and GTEA+ datasets-shows that the proposed model can significantly outperform
all unsupervised baselines and some supervised gaze prediction baselines.
Finally, we show that the model can also temporally segment egocentric videos
with a performance comparable to more complex, fully supervised deep learning
baselines.
