text: D.2 Frozen detection 
  
  We evaluate our model on the AVA (Gu et al., 2018) spatio-temporal localization of human actions dataset, containing 
  211k training and 57k validation video segments. We follow the experimental protocol of (Feichtenhofer et al., 2021), 
  and use precomputed masks from a pretrained Faster-RCNN adapted to videos, which uses a ResNeXt-101-FPN 
  backbone and is pretrained on ImageNet and COCO. We train a linear classifier on top of the frozen V-JEPA features 
  to classify the extracted regions of interest and report mean Average Precision (mAP) on the 60 most common 
  classes. Hyper-parameters are provided in Table 10. Our frozen features are obtained by concatenating the last layer 
  of the transformer encoder with three intermediate layers. We use a batch size of 64 and pretrain for 30 epochs with 
  AdamW using a learning rate of 0.0001 with 2 epochs of warmup and a weight decay of 0.05. 