However, 2D CNN on individual frames cannot model temporal information.ģD CNNs can jointly learn spatial and temporal features, while the computation cost for 3D CNN is large, making the production deployment expensive. For example, to distinguish between opening and closing a box, reversing the order will give opposite results, so temporal modeling is critical.Ī straightforward approach for video understanding is to directly use 2D CNN. One key difference between video recognition and image recognition is the need for temporal modeling.
Speed up tsm 4 full#
READ FULL TEXT VIEW PDFĭeep learning has become the standard for video understanding over the years. Something-Something V1 and V2 leaderboards upon this paper's submission. Remarkably, our framework ranks the first on both Measured on P100 GPU, our single modelĬompared to I3D. Modeling, we achieved better results than I3D family and ECO family using 6XĪnd 2.7X fewer FLOPs respectively. On the Something-Something-V1 dataset which focuses on temporal Into 2D CNNs to achieve temporal modeling at the cost of zero FLOPs and zero Of TSM is to shift part of the channels along the temporal dimension, whichįacilitates information exchange among neighboring frames. Specifically, it canĪchieve the performance of 3D CNN but maintain 2D complexity. That enjoys both high efficiency and high performance. In this paper, we propose a generic and effective Temporal Shift Module (TSM) Performance but are computationally intensive, making it expensive to deploy.
Conventional 2D CNNs are computationally cheap but cannotĬapture long-term temporal relationships 3D CNN based methods can achieve good The explosive growth in online video streaming gives rise to challenges onĮfficiently extracting the spatial-temporal information to perform video
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