  Exploring to what humans pay attention in dynamic panoramic scenes is useful
for many fundamental applications, including augmented reality (AR) in retail,
AR-powered recruitment, and visual language navigation. With this goal in mind,
we propose PV-SOD, a new task that aims to segment salient objects from
panoramic videos. In contrast to existing fixation-/object-level saliency
detection tasks, we focus on audio-induced salient object detection (SOD),
where the salient objects are labeled with the guidance of audio-induced eye
movements. To support this task, we collect the first large-scale dataset,
named ASOD60K, which contains 4K-resolution video frames annotated with a
six-level hierarchy, thus distinguishing itself with richness, diversity and
quality. Specifically, each sequence is marked with both its super-/sub-class,
with objects of each sub-class being further annotated with human eye
fixations, bounding boxes, object-/instance-level masks, and associated
attributes (e.g., geometrical distortion). These coarse-to-fine annotations
enable detailed analysis for PV-SOD modelling, e.g., determining the major
challenges for existing SOD models, and predicting scanpaths to study the
long-term eye fixation behaviors of humans. We systematically benchmark 11
representative approaches on ASOD60K and derive several interesting findings.
We hope this study could serve as a good starting point for advancing SOD
research towards panoramic videos. The dataset and benchmark will be made
publicly available at https://github.com/PanoAsh/ASOD60K.
