DEVA

DEVA: Tracking Anything with Decoupled Video Segmentation

Ho Kei Cheng, Seoung Wug Oh, Brian Price, Alexander Schwing, Joon-Young Lee

University of Illinois Urbana-Champaign and Adobe

ICCV 2023

[arXiV (coming soon)] [PDF] [Project Page]

Highlights

1. Provide long-term, open-vocabulary video segmentation with text-prompts out-of-the-box.
2. Fairly easy to integrate your own image model! Wouldn't you or your reviewers be interested in seeing examples where your image model also works well on videos :smirk:? No finetuning is needed!

Abstract

We develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we propose a (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several tasks, most notably in large-vocabulary video panoptic segmentation and open-world video segmentation.