ReferEverything: Towards Segmenting Everything We Can Speak of in Videos

• URL: http://arxiv.org/abs/2410.23287v1
• Date: Wed, 30 Oct 2024 17:59:26 GMT
• Title: ReferEverything: Towards Segmenting Everything We Can Speak of in Videos
• Authors: Anurag Bagchi, Zhipeng Bao, Yu-Xiong Wang, Pavel Tokmakov, Martial Hebert,
• Abstract summary: We present REM, a framework for segmenting concepts in video that can be described through natural language. Our method capitalizes on visual representations learned by video diffusion models on Internet-scale datasets.
• Score: 42.88584315033116
• License:
• Abstract: We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method capitalizes on visual-language representations learned by video diffusion models on Internet-scale datasets. A key insight of our approach is preserving as much of the generative model's original representation as possible, while fine-tuning it on narrow-domain Referral Object Segmentation datasets. As a result, our framework can accurately segment and track rare and unseen objects, despite being trained on object masks from a limited set of categories. Additionally, it can generalize to non-object dynamic concepts, such as waves crashing in the ocean, as demonstrated in our newly introduced benchmark for Referral Video Process Segmentation (Ref-VPS). Our experiments show that REM performs on par with state-of-the-art approaches on in-domain datasets, like Ref-DAVIS, while outperforming them by up to twelve points in terms of region similarity on out-of-domain data, leveraging the power of Internet-scale pre-training.

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