ReferEverything: Towards Segmenting Everything We Can Speak of in Videos
Authors: name: Anurag Bagchi, name: Zhipeng Bao, name: Yu-Xiong Wang, name: Pavel Tokmakov, name: Martial Hebert

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.