text: Evaluations. Pretrained models are evaluated on 
  downstream video and image tasks. On video tasks, 
  we use a subset of the VideoGLUE benchmark (Yuan 
  et al., 2023) to test for various capabilities; specif- 
  ically, we investigate action recognition on Kinetics- 
  400 (K400) (Kay et al., 2017), motion classification on 
  Something-Something-v2 (SSv2) (Goyal et al., 2017), 
  and action localization on AVA (Gu et al., 2018). Action 
  classification on Kinetics evaluates the appearance-based 
  understanding of the model, as many action classes in 
  the dataset can be inferred from the presence of specific 
  objects in the video (Sevilla-Lara et al., 2021). Motion 
  classification on Something-Something-v2 evaluates the 
  temporal understanding of the model, as action classes 
  in the dataset are decoupled from the appearance/pres- 
  ence of specific objects in the video (Goyal et al., 2017). 
  Finally, action localization on AVA evaluates the ability 
  of the model to understand and localize motions in the 
  video. We follow standard practice and report accu- 
  racy on K400 and SSv2 by sampling several spatial and 
  temporal views. For static image tasks, we explore ob- 
  ject recognition on ImageNet (Russakovsky et al., 2015), 
  scene classification on Places205 (Zhou et al., 2014), and 
  fine-grained recognition on iNaturalist 2021 (Van Horn 
  et al., 2018). 