1- While the experimental results suggest that the proposed approach is valuable for self-supervised learning on 360 video data which have spatial audio, little insights are given about why do we need to do self-supervised learning on this kind of data. In particular, 1) There are currently several large audio-video datasets such as HowTo100M and VIOLIN, 2) There is not much 360 video data on YouTube in comparison to normal data. 2- For the experimental comparisons, the authors at least should report the performance with using other self-supervised learning losses. For instance, masking features, predicting next video/audio feature, or reconstructing a feature. This will be very useful for understanding the importance of introduced loss in comparison with previous ones. 3- How the videos are divided into 10s segments? 4- It would be interesting to see how this spatial alignment works. For example, aligning an audio to the video and visualizing the corresponding visual region. 5- What's the impact of batch size on performance? batch size of 28 seems small to cover enough positive and negative samples. In this case, using MoCo loss instead of InfoNCE wouldn't help?
1- While the experimental results suggest that the proposed approach is valuable for self-supervised learning on 360 video data which have spatial audio, little insights are given about why do we need to do self-supervised learning on this kind of data. In particular,