speedup compared to the large pixel prediction models.
Comparison with image models. On tasks that re-
quire a fine-grained understanding of motion (Something-
Something-v2), theV-JEPA models provide a major im-
provement (over+21 points) compared to large-scale
image baselines, such as DINOv2, OpenCLIP, and I-
JEPA. Self-supervised pretraining from videos allows to
model dynamic concepts that are not easily learned from
static image datasets. Similarly, we observe that the
V-JEPA models outperform image-based pretraining on
action localization.
On Kinetics-400, we find image models to perform well;
e.g., while DINOv2 (Oquab et al., 2023) previously re-
ported 78.4% on K400 with a linear probe, we improve
the frozen evaluation of the g/14 model to83.4% by
using an attentive probe. In this case, our H/16 model
achieves 82.0% top-1 accuracy. It is worth noting that
the label for many Kinetics videos can be inferred using
appearance-based cues, without requiring an understand-
ing of motion (Sevilla-Lara et al., 2021).
The V-JEPA models narrow the gap with image models
on image classification tasks. In particular, V-JEPA
achieves a score of 77.4% on ImageNet using a one-
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