  Prior work has established test-time training (TTT) as a general framework to
further improve a trained model at test time. Before making a prediction on
each test instance, the model is trained on the same instance using a
self-supervised task, such as image reconstruction with masked autoencoders. We
extend TTT to the streaming setting, where multiple test instances - video
frames in our case - arrive in temporal order. Our extension is online TTT: The
current model is initialized from the previous model, then trained on the
current frame and a small window of frames immediately before. Online TTT
significantly outperforms the fixed-model baseline for four tasks, on three
real-world datasets. The relative improvement is 45% and 66% for instance and
panoptic segmentation. Surprisingly, online TTT also outperforms its offline
variant that accesses more information, training on all frames from the entire
test video regardless of temporal order. This differs from previous findings
using synthetic videos. We conceptualize locality as the advantage of online
over offline TTT. We analyze the role of locality with ablations and a theory
based on bias-variance trade-off.
