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Automatic saliency prediction in 360 videos is critical for viewpoint guidance applications (e.g., Facebook 360 Guide). We propose a spatial-temporal network which is (1) weakly-supervised trained and (2) tailor-made for 360 viewing sphere. Note that most existing methods are less scalable since they rely on annotated saliency map for training. Most importantly, they convert 360 sphere to 2D images (e.g., a single equirectangular image or multiple separate Normal Field-of-View (NFoV) images)arXiv:1806.01320v1 fatcat:dbrcy25zzrft5efopsr3ua2ble