Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos.

Clearly, with only 26M learnable parameters, the performance can be boosted from 79.9 to 84.4

Specifically, we introduce the notion of nearest simplex ETF geometry for the penultimate layer features at any given training iteration, by formulating it as a Riemannian optimisation.

They split every 2D image into a fixed number of patches, each of which is treated as a token.

In Table 4, we list the mCE of each corruption category. We apply our method to other network architectures and evaluate on the task of image classification. Datasets in Table 6 are the same as in Table 1. Intuitively, when testing on data whose distribution is "close" to the training data, using the main In this work, we take a naive measurement for the "closeness" of an ImageNet We process the entire ImageNet validation set using the visualization technique introduced in Section 3. We do not shuffle the ImageNet validation data when generating these batches. Table 8 shows the classification performance of various models on the two ImageNet-AdvBN variants, denoted as IN-Adv-VGG and IN-Adv-ResNet respectively.