Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to the limited memory.

Since the Resnet-18 feature extractor uses a ReLU activation function, the feature representation values are all non-negative, so the inputs to tukey's ladder of powers transformation are all valid. As expected, the performance of both methods drops a bit when the pre-training is not done on the similar classes. Still FeCAM outperforms NCM by about 10% on the final accuracy. In Algorithm 1, we present the pseudo code for using FeCAM classifier.Algorithm 1 FeCAM Require: Training data (D

Euclidean metric is suboptimal and that feature distributions are heterogeneous.