Temporal dynamics represent the evolving of video content over time, which are critical for action recognition. In this paper, we ask the question: can the off-the-shelf image transformer architecture learn temporal dynamics in videos? To this end, we propose Multidimensional Stacked Image (MSImage) as a new arrangement of video data, which can be fed to image transformers. Technically, MSImage is a high-resolution image that is composed of several evenly-sampled video clips stacked along the channel and space dimensions. The frames in each clip are concatenated along the channel dimension for the transformers to infer short-term dynamics. Meanwhile, the clips are arranged on different spatial positions for learning long-term dynamics. On this basis we propose MSImageFormer-a new variant of image transformer that takes MSImage as the input and is jointly optimized by video classification loss and a new dynamics enhancement loss. The network optimization attends to the high-frequency component of MSImage, avoiding overfitting to static visual patterns. We empirically demonstrate the merits of the MSImageFormer on six action recognition benchmarks. With only 2D image transformer as the classifier, our MSImageFormer achieves 85.3% and 69.7% top-1 accuracy on Kinetics-400 and Something-Something V2 datasets, respectively. Despite with fewer computations, the results are comparable to the SOTA 3D CNNs and video transformers.

Video is complex due to large variations in motion and rich content in fine-grained visual details. Abstracting useful information from such information-intensive media requires exhaustive computing resources. This paper studies a two-step alternative that first condenses the video sequence to an informative "frame" and then exploits off-the-shelf image recognition system on the synthetic frame. A valid question is how to define "useful information" and then distill it from a video sequence down to one synthetic frame. This paper presents a novel Informative Frame Synthesis (IFS) architecture that incorporates three objective tasks, i.e., appearance reconstruction, video categorization, motion estimation, and two regularizers, i.e., adversarial learning, color consistency. Each task equips the synthetic frame with one ability, while each regularizer enhances its visual quality. With these, by jointly learning the frame synthesis in an end-to-end manner, the generated frame is expected to encapsulate the required spatio-temporal information useful for video analysis. Extensive experiments are conducted on the large-scale Kinetics dataset. When comparing to baseline methods that map video sequence to a single image, IFS shows superior performance. More remarkably, IFS consistently demonstrates evident improvements on image-based 2D networks and clip-based 3D networks, and achieves comparable performance with the state-of-the-art methods with less computational cost.