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Article summary:
1. A spatial-temporal network is proposed for weakly-supervised saliency prediction in 360° videos.
2. Cube Padding (CP) technique is introduced to reduce distortion and image boundaries while being applicable to almost all Convolutional Neural Network (CNN) structures.
3. A new 360° video saliency dataset, Wild-360, is proposed to evaluate the method, which outperforms baseline methods in both speed and quality.
Article analysis:
The article provides a detailed description of the proposed Cube Padding (CP) technique for weakly-supervised saliency prediction in 360° videos. The authors provide evidence for their claims by introducing a new 360° video saliency dataset, Wild-360, which is used to evaluate the method and demonstrate its superiority over baseline methods in both speed and quality. The article does not appear to be biased or one-sided as it presents both sides of the argument equally. Furthermore, there are no unsupported claims or missing points of consideration as all claims are backed up with evidence from the experiments conducted on Wild-360 dataset. Additionally, there are no promotional content or partiality present in the article as it focuses solely on presenting the results of the experiments conducted on Wild-360 dataset without any bias towards any particular method or technique. Finally, possible risks associated with using CP technique are noted in the article as well as potential limitations that need to be addressed in future work.