UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model

• URL: http://arxiv.org/abs/2305.12659v2
• Date: Thu, 6 Jun 2024 05:57:51 GMT
• Title: UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
• Authors: Zhenghao Zhang, Shengfan Zhang, Zhichao Wei, Zuozhuo Dai, Siyu Zhu,
• Abstract summary: Segment Anything Model (SAM) introduces a new prompt-driven paradigm for image segmentation, offering new possibilities. We propose UVOSAM, a mask-free paradigm for UVOS that utilizes the STD-Net tracker. STD-Net incorporates a spatial-temporal decoupled deformable attention mechanism to establish an effective correlation between intra- and inter-frame features.
• Score: 5.632631449489529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The current state-of-the-art methods for unsupervised video object segmentation (UVOS) require extensive training on video datasets with mask annotations, limiting their effectiveness in handling challenging scenarios. However, the Segment Anything Model (SAM) introduces a new prompt-driven paradigm for image segmentation, offering new possibilities. In this study, we investigate SAM's potential for UVOS through different prompt strategies. We then propose UVOSAM, a mask-free paradigm for UVOS that utilizes the STD-Net tracker. STD-Net incorporates a spatial-temporal decoupled deformable attention mechanism to establish an effective correlation between intra- and inter-frame features, remarkably enhancing the quality of box prompts in complex video scenes. Extensive experiments on the DAVIS2017-unsupervised and YoutubeVIS19\&21 datasets demonstrate the superior performance of UVOSAM without mask supervision compared to existing mask-supervised methods, as well as its ability to generalize to weakly-annotated video datasets. Code can be found at https://github.com/alibaba/UVOSAM.

Related papers

• AU-vMAE: Knowledge-Guide Action Units Detection via Video Masked Autoencoder [38.04963261966939]
  We propose a video-level pre-training scheme for facial action units (FAU) detection. At the heart of our design is a pre-trained video feature extractor based on the video-masked autoencoder. Our approach demonstrates substantial enhancement in performance compared to the existing state-of-the-art methods used in BP4D and DISFA FAUs datasets.
  arXiv  Detail & Related papers  (2024-07-16T08:07:47Z)
• MAS-SAM: Segment Any Marine Animal with Aggregated Features [55.91291540810978]
  We propose a novel feature learning framework named MAS-SAM for marine animal segmentation. Our method enables to extract richer marine information from global contextual cues to fine-grained local details.
  arXiv  Detail & Related papers  (2024-04-24T07:38:14Z)
• SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising [37.216493829454706]
  We explore the potential of applying the Segment Anything Model to track and segment objects in videos. Specifically, we iteratively propagate the bounding box of each object's mask in the preceding frame as the prompt for the next frame. To enhance SAM's denoising capability against position and size variations, we propose a multi-prompt strategy.
  arXiv  Detail & Related papers  (2024-03-07T03:52:59Z)
• Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation [76.68301884987348]
  We propose a simple yet effective approach for self-supervised video object segmentation (VOS) Our key insight is that the inherent structural dependencies present in DINO-pretrained Transformers can be leveraged to establish robust-temporal segmentation correspondences in videos. Our method demonstrates state-of-the-art performance across multiple unsupervised VOS benchmarks and excels in complex real-world multi-object video segmentation tasks.
  arXiv  Detail & Related papers  (2023-11-29T18:47:17Z)
• Leveraging Foundation models for Unsupervised Audio-Visual Segmentation [49.94366155560371]
  Audio-Visual (AVS) aims to precisely outline audible objects in a visual scene at the pixel level. Existing AVS methods require fine-grained annotations of audio-mask pairs in supervised learning fashion. We introduce unsupervised audio-visual segmentation with no need for task-specific data annotations and model training.
  arXiv  Detail & Related papers  (2023-09-13T05:05:47Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration [13.284951215948052]
  We present a novel weakly supervised framework with textbfS-patiotextbfTemporal textbfClaboration for instance textbfSegmentation in videos. Our method achieves strong performance and even outperforms fully supervised TrackR-CNN and MaskTrack R-CNN.
  arXiv  Detail & Related papers  (2022-12-15T02:44:13Z)
• Self-supervised Amodal Video Object Segmentation [57.929357732733926]
  Amodal perception requires inferring the full shape of an object that is partially occluded. This paper develops a new framework of amodal Video object segmentation (SaVos)
  arXiv  Detail & Related papers  (2022-10-23T14:09:35Z)
• Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos [159.02703673838639]
  We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.
  arXiv  Detail & Related papers  (2021-01-06T18:56:24Z)

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.