text: In Table 15, we evaluate V-JEPA using finetuning (separately) on K400 and SSv2. We compare V-JEPA with 
  VideoMAEv2 (Wang et al., 2023a), VideoMAE (Tong et al., 2022) and MVD (Wang et al., 2023b) using a ViT-L/16 
  or a ViT-H/16 architecture. V-JEPA obtains competitive performance using a finetuning protocol. With a ViTiH/16 
  architecture, V-JEPA outperforms by 1.2% VideoMAE and +0.3% VideoMAEv2 on the SSv2 dataset, while obtaining 
  comparable performance on K400. V-JEPA also obtains performance similar to MVD on the SSv2 dataset. The 
  MVD model achieves the best performance across models on the K400 dataset, and is trained using the image 
  dataset ImageNet1K, in contrast to the other methods in the table, which only use video data. Additionally MVD 
  requires the processing of significantly more samples during pretraining due to the cost of training the teacher 
  encoder networks in a pre-pre-training step. 