Weakness
1 The novelty is limited. The low-rank design is relevant to (Hu et al., 2021). The sparse design is similar to Taylor pruning (Molchanov et al., 2019).
2 The experiments are not quite convincing. The authors choose the old baseline like R3D and C3D. To reduce computation complexity, many papers have been proposed in 3D CNN (X3D, SlowFast, etc). Does the proposed method also works on these 3D CNNs? Or compared to these approaches, what is the advantage of the proposed method?
3 The paper is hard to follow. In fact, I have to read many times to get what it is. I understand it is a theoretical-kind paper. But please further explain the mathmetical formulation clearly to show why and how it works.
2 The experiments are not quite convincing. The authors choose the old baseline like R3D and C3D. To reduce computation complexity, many papers have been proposed in 3D CNN (X3D, SlowFast, etc). Does the proposed method also works on these 3D CNNs? Or compared to these approaches, what is the advantage of the proposed method?