FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution

• URL: http://arxiv.org/abs/2504.09507v1
• Date: Sun, 13 Apr 2025 10:14:19 GMT
• Title: FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution
• Authors: Mengjiao Wang, Junpei Zhang, Xu Liu, Yuting Yang, Mengru Ma,
• Abstract summary: Video Object PV (VOS) is one of the most fundamental and challenging tasks in computer vision.<n>This paper aims to achieve accurate segmentation of video objects in challenging scenes.
• Score: 2.9149767401557574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Object Segmentation (VOS) is one of the most fundamental and challenging tasks in computer vision and has a wide range of applications. Most existing methods rely on spatiotemporal memory networks to extract frame-level features and have achieved promising results on commonly used datasets. However, these methods often struggle in more complex real-world scenarios. This paper addresses this issue, aiming to achieve accurate segmentation of video objects in challenging scenes. We propose fine-tuning VOS (FVOS), optimizing existing methods for specific datasets through tailored training. Additionally, we introduce a morphological post-processing strategy to address the issue of excessively large gaps between adjacent objects in single-model predictions. Finally, we apply a voting-based fusion method on multi-scale segmentation results to generate the final output. Our approach achieves J&F scores of 76.81% and 83.92% during the validation and testing stages, respectively, securing third place overall in the MOSE Track of the 4th PVUW challenge 2025.

Related papers

• 3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation [63.199793919573295]
  Video Object (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiration from the Cutie model, and we investigate the effects of object memory, the total number of memory frames, and input resolution on segmentation performance.
  arXiv  Detail & Related papers  (2024-06-06T00:56:25Z)
• 1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation [65.45702890457046]
  We integrate strengths of leading RVOS models to build up an effective paradigm. To improve the consistency and quality of masks, we propose Two-Stage Multi-Model Fusion strategy. Our method achieves 75.7% J&F on Ref-Youtube-VOS validation set and 70% J&F on test set, which ranks 1st place on 5th Large-scale Video Object Challenge (ICCV 2023) track 3.
  arXiv  Detail & Related papers  (2024-01-01T04:24:48Z)
• M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection [22.60675416709486]
  M$3$Net is an attention network for Salient Object Detection. Cross-attention approach to achieve the interaction between multilevel features. Mixed Attention Block aims at modeling context at both global and local levels. Multilevel supervision strategy to optimize the aggregated feature stage-by-stage.
  arXiv  Detail & Related papers  (2023-09-15T12:46:14Z)
• MOSE: A New Dataset for Video Object Segmentation in Complex Scenes [106.64327718262764]
  Video object segmentation (VOS) aims at segmenting a particular object throughout the entire video clip sequence. The state-of-the-art VOS methods have achieved excellent performance (e.g., 90+% J&F) on existing datasets. We collect a new VOS dataset called coMplex video Object SEgmentation (MOSE) to study the tracking and segmenting objects in complex environments.
  arXiv  Detail & Related papers  (2023-02-03T17:20:03Z)
• 5th Place Solution for YouTube-VOS Challenge 2022: Video Object Segmentation [4.004851693068654]
  Video object segmentation (VOS) has made significant progress with the rise of deep learning. Similar objects are easily confused and tiny objects are difficult to find. We propose a simple yet effective solution for this task.
  arXiv  Detail & Related papers  (2022-06-20T06:14:27Z)
• 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)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)
• Learning Fast and Robust Target Models for Video Object Segmentation [83.3382606349118]
  Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. Most previous approaches fine-tune segmentation networks on the first frame, resulting in impractical frame-rates and risk of overfitting. We propose a novel VOS architecture consisting of two network components.
  arXiv  Detail & Related papers  (2020-02-27T21:58:06Z)

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.