  Visual search is important in our daily life. The efficient allocation of
visual attention is critical to effectively complete visual search tasks. Prior
research has predominantly modelled the spatial allocation of visual attention
in images at the pixel level, e.g. using a saliency map. However, emerging
evidence shows that visual attention is guided by objects rather than pixel
intensities. This paper introduces the Object-level Attention Transformer
(OAT), which predicts human scanpaths as they search for a target object within
a cluttered scene of distractors. OAT uses an encoder-decoder architecture. The
encoder captures information about the position and appearance of the objects
within an image and about the target. The decoder predicts the gaze scanpath as
a sequence of object fixations, by integrating output features from both the
encoder and decoder. We also propose a new positional encoding that better
reflects spatial relationships between objects. We evaluated OAT on the Amazon
book cover dataset and a new dataset for visual search that we collected. OAT's
predicted gaze scanpaths align more closely with human gaze patterns, compared
to predictions by algorithms based on spatial attention on both established
metrics and a novel behavioural-based metric. Our results demonstrate the
generalization ability of OAT, as it accurately predicts human scanpaths for
unseen layouts and target objects.
