Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations

• URL: http://arxiv.org/abs/2308.11796v1
• Date: Tue, 22 Aug 2023 21:28:58 GMT
• Title: Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
• Authors: Mohammadreza Salehi, Efstratios Gavves, Cees G. M. Snoek, Yuki M. Asano
• Abstract summary: We propose a novel approach that incorporates temporal consistency in dense self-supervised learning. 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. Time-tuning improves the state-of-the-art by 8-10% for unsupervised semantic segmentation on videos and matches it for images.
• Score: 79.87044240860466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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