This novel framework is then extended to a hierarchical structure, where uncorrelated tokens at different stages are gradually removed, resulting in a considerable shrinkage of computational cost. We include extensive experiments on both image and video tasks, where our method could deliver up to 1.4

Partial Fine-tuning: we finetune only the last Transformer block and the classifier layer. Experiments with other foundation models It is observed that with the same model, CLIP pre-training is superior to ImageNet21K pre-training (not surprising due to the training data scale and richness difference). Inference Speed We provide an inference speed test in Table 4. All other training settings are identical to that used for Kinetics-400. We compare with methods that take only RGB frames as input (without optical flow).

Due to the ever-growing model size, the standard full fine-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage.

Using a compressor-based distance metric derived from Kolmogorov complexity, together with few labeled data, NPC-L V classifies without further training.

In the "modern era", such model-parallel training techniques trace their roots back to AlexNet [