Summary of Stitchfusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation, by Bingyu Li et al.
StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation
by Bingyu Li, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed StitchFusion framework is a straightforward yet effective modal fusion approach that integrates large-scale pre-trained models as encoders and feature fusers, enabling comprehensive multi-modal and multi-scale feature fusion for multimodal semantic segmentation tasks. By leveraging the sharing of multi-modal visual information during encoding and introducing a multi-directional adapter module (MultiAdapter) to facilitate cross-modal information transfer, StitchFusion achieves state-of-the-art performance on four multi-modal segmentation datasets with minimal additional parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StitchFusion is a new way to combine different types of images together to get better results. It uses pre-trained models that are good at recognizing things in pictures and combines them in a special way to work well with lots of different types of images. This makes it really good at recognizing things in complex scenes, which is important for applications like self-driving cars. |
Keywords
* Artificial intelligence * Multi modal * Semantic segmentation
