  Despite recent advances in video action recognition achieving strong
performance on existing benchmarks, these models often lack robustness when
faced with natural distribution shifts between training and test data. We
propose two novel evaluation methods to assess model resilience to such
distribution disparity. One method uses two different datasets collected from
different sources and uses one for training and validation, and the other for
testing. More precisely, we created dataset splits of HMDB-51 or UCF-101 for
training, and Kinetics-400 for testing, using the subset of the classes that
are overlapping in both train and test datasets. The other proposed method
extracts the feature mean of each class from the target evaluation dataset's
training data (i.e. class prototype) and estimates test video prediction as a
cosine similarity score between each sample to the class prototypes of each
target class. This procedure does not alter model weights using the target
dataset and it does not require aligning overlapping classes of two different
datasets, thus is a very efficient method to test the model robustness to
distribution shifts without prior knowledge of the target distribution. We
address the robustness problem by adversarial augmentation training -
generating augmented views of videos that are "hard" for the classification
model by applying gradient ascent on the augmentation parameters - as well as
"curriculum" scheduling the strength of the video augmentations. We
experimentally demonstrate the superior performance of the proposed adversarial
augmentation approach over baselines across three state-of-the-art action
recognition models - TSM, Video Swin Transformer, and Uniformer. The presented
work provides critical insight into model robustness to distribution shifts and
presents effective techniques to enhance video action recognition performance
in a real-world deployment.
