Summary of 1st Place Solution For 5th Lsvos Challenge: Referring Video Object Segmentation, by Zhuoyan Luo et al.
1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation
by Zhuoyan Luo, Yicheng Xiao, Yong Liu, Yitong Wang, Yansong Tang, Xiu Li, Yujiu Yang
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to Referring Video Object Segmentation (RVOS) is proposed, which integrates the strengths of leading RVOS models and builds an effective paradigm for generating segmentation masks. The approach obtains binary mask sequences from RVOS models and uses a Two-Stage Multi-Model Fusion strategy to improve consistency and quality. This method achieves state-of-the-art performance on the Ref-Youtube-VOS validation set (75.7% J&F) and test set (70% J&F), ranking 1st place on the 5th Large-scale Video Object Segmentation Challenge (ICCV 2023) track 3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to identify objects in videos, which is better than previous methods. They take multiple approaches and combine them to make it work well. This helps improve the accuracy of object detection in videos. Their method did really well on a test set, beating all other methods that tried this challenge. |
Keywords
» Artificial intelligence » Mask » Object detection
