Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation

• URL: http://arxiv.org/abs/2308.13505v1
• Date: Fri, 25 Aug 2023 17:30:08 GMT
• Title: Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation
• Authors: Jiaming Zhang, Yutao Cui, Gangshan Wu, Limin Wang
• Abstract summary: Current prevailing Video Object (VOS) methods usually perform dense matching between the current and reference frames after extracting features. We propose a unified VOS framework, coined as JointFormer, for joint modeling of the three elements of feature, correspondence, and a compressed memory.
• Score: 52.11279360934703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current prevailing Video Object Segmentation (VOS) methods usually perform dense matching between the current and reference frames after extracting their features. One on hand, the decoupled modeling restricts the targets information propagation only at high-level feature space. On the other hand, the pixel-wise matching leads to a lack of holistic understanding of the targets. To overcome these issues, we propose a unified VOS framework, coined as JointFormer, for joint modeling the three elements of feature, correspondence, and a compressed memory. The core design is the Joint Block, utilizing the flexibility of attention to simultaneously extract feature and propagate the targets information to the current tokens and the compressed memory token. This scheme allows to perform extensive information propagation and discriminative feature learning. To incorporate the long-term temporal targets information, we also devise a customized online updating mechanism for the compressed memory token, which can prompt the information flow along the temporal dimension and thus improve the global modeling capability. Under the design, our method achieves a new state-of-art performance on DAVIS 2017 val/test-dev (89.7% and 87.6%) and YouTube-VOS 2018/2019 val (87.0% and 87.0%) benchmarks, outperforming existing works by a large margin.

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