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Space Time Recurrent Memory Network
[article]
2022
arXiv
pre-print
Transformers have recently been popular for learning and inference in the spatial-temporal domain. However, their performance relies on storing and applying attention to the feature tensor of each frame in video. Hence, their space and time complexity increase linearly as the length of video grows, which could be very costly for long videos. We propose a novel visual memory network architecture for the learning and inference problem in the spatial-temporal domain. We maintain a fixed set of
arXiv:2109.06474v2
fatcat:gz5epmyrzveq3bmsde26gy7viy