Summary of Multi-level Aggregation and Recursive Alignment Architecture For Efficient Parallel Inference Segmentation Network, by Yanhua Zhang et al.
Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network
by Yanhua Zhang, Ke Zhang, Jingyu Wang, Yulin Wu, Wuwei Wang
First submitted to arxiv on: 3 Feb 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 parallel inference network, MFARANet, is a real-time semantic segmentation framework that achieves a good trade-off between speed and accuracy. It employs a shallow backbone to ensure real-time processing while compensating for the reduced model capacity with three core components: Multi-level Feature Aggregation Module (MFAM), Recursive Alignment Module (RAM), and Adaptive Scores Fusion Module (ASFM). These components work together to aggregate features, align multi-scale feature maps, and fuse scores from independent parallel inference. The framework outperforms state-of-the-art real-time methods on Cityscapes and CamVid datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MFARANet is a new way to do semantic segmentation quickly and accurately. It’s like a superpower for computers that helps them understand what they see in the world. Right now, most computer vision models are either really fast but not very good at getting things right or really good but take forever to work. MFARANet tries to find a balance between speed and accuracy by using special tricks like combining features from different levels and aligning information from different parts of an image. |
Keywords
* Artificial intelligence * Alignment * Inference * Semantic segmentation
