Spatial-Temporal Multi-level Association for Video Object Segmentation

• URL: http://arxiv.org/abs/2404.06265v1
• Date: Tue, 9 Apr 2024 12:44:34 GMT
• Title: Spatial-Temporal Multi-level Association for Video Object Segmentation
• Authors: Deshui Miao, Xin Li, Zhenyu He, Huchuan Lu, Ming-Hsuan Yang,
• Abstract summary: This paper proposes spatial-temporal multi-level association, which jointly associates reference frame, test frame, and object features. Specifically, we construct a spatial-temporal multi-level feature association module to learn better target-aware features.
• Score: 89.32226483171047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing semi-supervised video object segmentation methods either focus on temporal feature matching or spatial-temporal feature modeling. However, they do not address the issues of sufficient target interaction and efficient parallel processing simultaneously, thereby constraining the learning of dynamic, target-aware features. To tackle these limitations, this paper proposes a spatial-temporal multi-level association framework, which jointly associates reference frame, test frame, and object features to achieve sufficient interaction and parallel target ID association with a spatial-temporal memory bank for efficient video object segmentation. Specifically, we construct a spatial-temporal multi-level feature association module to learn better target-aware features, which formulates feature extraction and interaction as the efficient operations of object self-attention, reference object enhancement, and test reference correlation. In addition, we propose a spatial-temporal memory to assist feature association and temporal ID assignment and correlation. We evaluate the proposed method by conducting extensive experiments on numerous video object segmentation datasets, including DAVIS 2016/2017 val, DAVIS 2017 test-dev, and YouTube-VOS 2018/2019 val. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of our approach. All source code and trained models will be made publicly available.

Related papers

• Learning Spatial-Semantic Features for Robust Video Object Segmentation [108.045326229865]
  We propose a robust video object segmentation framework equipped with spatial-semantic features and discriminative object queries. We show that the proposed method set a new state-of-the-art performance on multiple datasets.
  arXiv  Detail & Related papers  (2024-07-10T15:36:00Z)
• Training-Free Robust Interactive Video Object Segmentation [82.05906654403684]
  We propose a training-free prompt tracking framework for interactive video object segmentation (I-PT) We jointly adopt sparse points and boxes tracking, filtering out unstable points and capturing object-wise information. Our framework has demonstrated robust zero-shot video segmentation results on popular VOS datasets.
  arXiv  Detail & Related papers  (2024-06-08T14:25:57Z)
• Video Object Segmentation with Dynamic Query Modulation [23.811776213359625]
  We propose a query modulation method, termed QMVOS, for object and multi-object segmentation. Our method can bring significant improvements to the memory-based SVOS method and achieve competitive performance on standard SVOS benchmarks.
  arXiv  Detail & Related papers  (2024-03-18T07:31:39Z)
• TIVE: A Toolbox for Identifying Video Instance Segmentation Errors [5.791075969487935]
  Video Instance Errors(VIS) task has attracted vast researchers' focus on architecture modeling to boost performance. We introduce TIVE, a toolbox for identifying Video instance segmentation errors. We conduct extensive experiments by the toolbox to further illustrate how spatial segmentation and temporal association affect each other.
  arXiv  Detail & Related papers  (2022-10-17T08:51:31Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching [67.02962970820505]
  We introduce "tracking-by-detection" into Video Object (VOS) We propose a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. We achieve new state-of-the-art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J&F measure of 75.9% respectively.
  arXiv  Detail & Related papers  (2020-07-11T05:44:16Z)
• Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos [85.6430597108455]
  We propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos. It captures the common salient foreground regions among video frames and explores the spatial-temporal long-range context interdependency from such regions. Multiple spatialtemporal interaction modules within CSTNet are proposed, which exploit the spatial and temporal long-range context interdependencies on such features and spatial-temporal information correlation.
  arXiv  Detail & Related papers  (2020-04-10T10:23:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.