Summary of Learning Spatial-semantic Features For Robust Video Object Segmentation, by Xin Li et al.
Learning Spatial-Semantic Features for Robust Video Object Segmentation
by Xin Li, Deshui Miao, Zhenyu He, Yaowei Wang, Huchuan Lu, Ming-Hsuan Yang
First submitted to arxiv on: 10 Jul 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 video object segmentation framework is proposed to tackle the challenges of tracking and segmenting multiple similar objects with complex or separate parts in long-term videos. The framework leverages spatial-semantic features and discriminative object queries, combining a semantic embedding block and spatial dependencies modeling block to provide a comprehensive target representation. Additionally, a masked cross-attention module generates object queries that focus on the most discriminative parts of target objects during query propagation, alleviating noise accumulation. The proposed method achieves state-of-the-art performance on multiple datasets, including DAVIS2017 test (89.1%), YoutubeVOS 2019 (88.5%), MOSE (75.1%), LVOS test (73.0%), and LVOS val (75.1%). All source code and trained models will be made publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in video analysis: identifying multiple similar objects with complicated parts over long periods of time. It’s hard because the parts can be hidden, or confused with other things in the background. The solution uses special features that combine where things are and what they mean, along with a way to focus on the most important details. This makes it much better at finding and following these objects. The method does really well on several tests, showing how good it is. |
Keywords
» Artificial intelligence » Cross attention » Embedding » Tracking
