Disentangling Spatial and Temporal Learning for Efficient Image-to-Video
  Transfer Learning
Zhiwu Qing Shiwei Zhang Ziyuan Huang Yingya Zhang Changxin Gao Deli Zhao Nong Sang
Recently, large-scale pre-trained language-image models like CLIP have shown
extraordinary capabilities for understanding spatial contents, but naively
transferring such models to video recognition still suffers from unsatisfactory
temporal modeling capabilities. Existing methods insert tunable structures into
or in parallel with the pre-trained model, which either requires
back-propagation through the whole pre-trained model and is thus
resource-demanding, or is limited by the temporal reasoning capability of the
pre-trained structure. In this work, we present DiST, which disentangles the
learning of spatial and temporal aspects of videos. Specifically, DiST uses a
dual-encoder structure, where a pre-trained foundation model acts as the
spatial encoder, and a lightweight network is introduced as the temporal
encoder. An integration branch is inserted between the encoders to fuse
spatio-temporal information. The disentangled spatial and temporal learning in
DiST is highly efficient because it avoids the back-propagation of massive
pre-trained parameters. Meanwhile, we empirically show that disentangled
learning with an extra network for integration benefits both spatial and
temporal understanding. Extensive experiments on five benchmarks show that DiST
delivers better performance than existing state-of-the-art methods by
convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve
89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability
of DiST. Codes and models can be found in
https://github.com/alibaba-mmai-research/DiST.