Nowadays, 360° video/image has been increasingly popular and drawn great attention. The
spherical viewing range of 360° video/image accounts for huge data, which pose the …

With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

In this paper we challenge the common assumption that convolutional layers in modern
CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial …

We present a significantly improved data‐driven global weather forecasting framework using
a deep convolutional neural network (CNN) to forecast several basic atmospheric variables …

A well-known challenge in applying deep-learning methods to omnidirectional images is
spherical distortion. In dense regression tasks such as depth estimation, where structural …

We present HoHoNet, a versatile and efficient framework for holistic understanding of an
indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact …

Partial convolution weights convolutions with binary masks and renormalizes on valid pixels.
It was originally proposed for image inpainting task because a corrupted image processed …

Depth estimation from a monocular 360 image is an emerging problem that gains popularity
due to the availability of consumer-level 360 cameras and the complete surrounding …

We introduce a novel self-supervised pretext task for learning representations from audio-
visual content. Prior work on audio-visual representation learning leverages …

Abstract 360deg videos convey holistic views for the surroundings of a scene. It provides
audio-visual cues beyond predetermined normal field of views and displays distinctive …