Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation

• URL: http://arxiv.org/abs/2311.17893v2
• Date: Mon, 8 Jul 2024 05:33:12 GMT
• Title: Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
• Authors: Shuangrui Ding, Rui Qian, Haohang Xu, Dahua Lin, Hongkai Xiong,
• Abstract summary: 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.
• Score: 76.68301884987348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, 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 spatio-temporal correspondences in videos. Furthermore, simple clustering on this correspondence cue is sufficient to yield competitive segmentation results. Previous self-supervised VOS techniques majorly resort to auxiliary modalities or utilize iterative slot attention to assist in object discovery, which restricts their general applicability and imposes higher computational requirements. To deal with these challenges, we develop a simplified architecture that capitalizes on the emerging objectness from DINO-pretrained Transformers, bypassing the need for additional modalities or slot attention. Specifically, we first introduce a single spatio-temporal Transformer block to process the frame-wise DINO features and establish spatio-temporal dependencies in the form of self-attention. Subsequently, utilizing these attention maps, we implement hierarchical clustering to generate object segmentation masks. To train the spatio-temporal block in a fully self-supervised manner, we employ semantic and dynamic motion consistency coupled with entropy normalization. Our method demonstrates state-of-the-art performance across multiple unsupervised VOS benchmarks and particularly excels in complex real-world multi-object video segmentation tasks such as DAVIS-17-Unsupervised and YouTube-VIS-19. The code and model checkpoints will be released at https://github.com/shvdiwnkozbw/SSL-UVOS.

Related papers

• Spatial-Temporal Multi-level Association for Video Object Segmentation [89.32226483171047]
  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.
  arXiv  Detail & Related papers  (2024-04-09T12:44:34Z)
• Appearance-based Refinement for Object-Centric Motion Segmentation [95.80420062679104]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a simple selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTubeVOS, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Self-supervised Object-Centric Learning for Videos [39.02148880719576]
  We propose the first fully unsupervised method for segmenting multiple objects in real-world sequences. Our object-centric learning framework spatially binds objects to slots on each frame and then relates these slots across frames. Our method can successfully segment multiple instances of complex and high-variety classes in YouTube videos.
  arXiv  Detail & Related papers  (2023-10-10T18:03:41Z)
• FODVid: Flow-guided Object Discovery in Videos [12.792602427704395]
  We focus on building a generalizable solution that avoids overfitting to the individual intricacies. To solve Video Object (VOS) in an unsupervised setting, we propose a new pipeline (FODVid) based on the idea of guiding segmentation outputs.
  arXiv  Detail & Related papers  (2023-07-10T07:55:42Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)
• Video Frame Interpolation Transformer [86.20646863821908]
  We propose a Transformer-based video framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations. To avoid the high computational cost of global self-attention, we introduce the concept of local attention into video. In addition, we develop a multi-scale frame scheme to fully realize the potential of Transformers.
  arXiv  Detail & Related papers  (2021-11-27T05:35:10Z)
• Self-supervised Video Object Segmentation by Motion Grouping [79.13206959575228]
  We develop a computer vision system able to segment objects by exploiting motion cues. We introduce a simple variant of the Transformer to segment optical flow frames into primary objects and the background. We evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2, and FBMS59)
  arXiv  Detail & Related papers  (2021-04-15T17:59:32Z)
• SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation [24.884078497381633]
  We introduce a Transformer-based approach to video object segmentation (VOS) Our attention-based approach allows a model to learn to attend over a history features of multiple frames. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness compared with the state of the art.
  arXiv  Detail & Related papers  (2021-01-21T20:06:12Z)
• Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation [27.559093073097483]
  Current approaches for Semi-supervised Video Object (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. We exploit this observation by using temporal information to quickly identify frames with minimal change. We propose a novel dynamic network that estimates change across frames and decides which path -- computing a full network or reusing previous frame's feature -- to choose.
  arXiv  Detail & Related papers  (2020-12-21T19:40:17Z)

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.