  Spatially dense self-supervised learning is a rapidly growing problem domain
with promising applications for unsupervised segmentation and pretraining for
dense downstream tasks. Despite the abundance of temporal data in the form of
videos, this information-rich source has been largely overlooked. Our paper
aims to address this gap by proposing a novel approach that incorporates
temporal consistency in dense self-supervised learning. While methods designed
solely for images face difficulties in achieving even the same performance on
videos, our method improves not only the representation quality for videos-but
also images. Our approach, which we call time-tuning, starts from
image-pretrained models and fine-tunes them with a novel self-supervised
temporal-alignment clustering loss on unlabeled videos. This effectively
facilitates the transfer of high-level information from videos to image
representations. Time-tuning improves the state-of-the-art by 8-10% for
unsupervised semantic segmentation on videos and matches it for images. We
believe this method paves the way for further self-supervised scaling by
leveraging the abundant availability of videos. The implementation can be found
here : https://github.com/SMSD75/Timetuning
