Summary of 3rd Place Solution For Mose Track in Cvpr 2024 Pvuw Workshop: Complex Video Object Segmentation, by Xinyu Liu et al.
3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation
by Xinyu Liu, Jing Zhang, Kexin Zhang, Yuting Yang, Licheng Jiao, Shuyuan Yang
First submitted to arxiv on: 6 Jun 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 This paper investigates the impact of various factors on video object segmentation (VOS) performance, building upon the Cutie model’s inspiration. The study explores the effects of object memory, total number of memory frames, and input resolution on segmentation accuracy. The research validates its inference method on the MOSE dataset, which features complex occlusions. The findings demonstrate a J&F score of 0.8139 on the test set, placing the approach third in the final ranking. The results highlight the robustness and accuracy of the method in handling challenging VOS scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to identify objects in videos. It’s like trying to find a specific cat in a bunch of movies. The researchers wanted to see how well their new way of doing this works, so they tested it on some tricky video clips. They found that their method does a great job of picking out the important parts of the video, even when there are lots of distractions. This is important because it could help us create better video editing software and more realistic special effects. |
Keywords
» Artificial intelligence » Inference
