text: Specifically, we first divide a video in 8 (or 2) equal-length temporal segments, and sample 1 clip at random per 
  segment. The video encoder Eθ processes each clip separately and produces a clip-level feature map. The feature 
  maps for each clip are then concatenated together and fed to the attentive probe. At test time, we average the 
  prediction of 3 spatial views following standard practice in video classification. 
  
  Application of video models to images. To evaluate the video models on image tasks, we simply duplicate input 
  images to generate still video clips of 16 frames. We perform this duplication operation simply for convenience in 
  evaluation of the video models, however we find this step to be unnecessary in general. Given a video tokenizer 
  implemented as a 3D-conv with a temporal stride of 2, it is sufficient to simply duplicate the image into a 2 frame 
  video clip. This would result in the same number of input tokens as that produced by a static image model with a 
  2D-conv tokenizer. 
  
  Application of image models to videos. To evaluate image models such as DINOv2 and OpenCLIP on video tasks, 
  we simply process each frame independently with the image encoder to produce a frame-level feature map. The 
  feature maps for each frame are then concatenated and fed to the attentive probe, just as we do with the clip-level 
  feature maps when evaluating video models. 