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DVIS++: Improved Decoupled Framework for Universal Video Segmentation (2312.13305v1)

Published 20 Dec 2023 in cs.CV

Abstract: We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video semantic segmentation (VSS), and video panoptic segmentation (VPS). Unlike previous methods that model video segmentation in an end-to-end manner, our approach decouples video segmentation into three cascaded sub-tasks: segmentation, tracking, and refinement. This decoupling design allows for simpler and more effective modeling of the spatio-temporal representations of objects, especially in complex scenes and long videos. Accordingly, we introduce two novel components: the referring tracker and the temporal refiner. These components track objects frame by frame and model spatio-temporal representations based on pre-aligned features. To improve the tracking capability of DVIS, we propose a denoising training strategy and introduce contrastive learning, resulting in a more robust framework named DVIS++. Furthermore, we evaluate DVIS++ in various settings, including open vocabulary and using a frozen pre-trained backbone. By integrating CLIP with DVIS++, we present OV-DVIS++, the first open-vocabulary universal video segmentation framework. We conduct extensive experiments on six mainstream benchmarks, including the VIS, VSS, and VPS datasets. Using a unified architecture, DVIS++ significantly outperforms state-of-the-art specialized methods on these benchmarks in both close- and open-vocabulary settings. Code:~\url{https://github.com/zhang-tao-whu/DVIS_Plus}.

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Analysis of "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals"

The paper "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" authored by Michael Shell et al., serves as a demonstrative example for preparing submissions in IEEE Computer Society journals using the IEEEtran LaTeX class file. The paper is essentially an indicative template, crafted to guide authors in structuring and formatting their manuscripts in compliance with the IEEE's publication standards.

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Authors (10)
  1. Tao Zhang (481 papers)
  2. Xingye Tian (6 papers)
  3. Yikang Zhou (7 papers)
  4. Shunping Ji (23 papers)
  5. Xuebo Wang (6 papers)
  6. Xin Tao (50 papers)
  7. Yuan Zhang (331 papers)
  8. Pengfei Wan (86 papers)
  9. Zhongyuan Wang (105 papers)
  10. Yu Wu (196 papers)
Citations (11)
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GitHub

  1. GitHub - zhang-tao-whu/DVIS: DVIS: Decoupled Video Instance Segmentation Framework (124 stars)