  We study the task of robust feature representations, aiming to generalize
well on multiple datasets for action recognition. We build our method on
Transformers for its efficacy. Although we have witnessed great progress for
video action recognition in the past decade, it remains challenging yet
valuable how to train a single model that can perform well across multiple
datasets. Here, we propose a novel multi-dataset training paradigm, MultiTrain,
with the design of two new loss terms, namely informative loss and projection
loss, aiming to learn robust representations for action recognition. In
particular, the informative loss maximizes the expressiveness of the feature
embedding while the projection loss for each dataset mines the intrinsic
relations between classes across datasets. We verify the effectiveness of our
method on five challenging datasets, Kinetics-400, Kinetics-700,
Moments-in-Time, Activitynet and Something-something-v2 datasets. Extensive
experimental results show that our method can consistently improve
state-of-the-art performance. Code and models are released.

Computer Vision and Pattern Recognition