Summary of Segformer++: Efficient Token-merging Strategies For High-resolution Semantic Segmentation, by Daniel Kienzle et al.
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
by Daniel Kienzle, Marco Kantonis, Robin Schön, Rainer Lienhart
First submitted to arxiv on: 23 May 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 This paper addresses a challenge in utilizing transformer architectures for semantic segmentation of high-resolution images by decreasing the number of tokens through token merging. This approach has been shown to significantly enhance inference speed, training efficiency, and memory utilization for image classification tasks. The authors explore various token merging strategies within the Segformer architecture and perform experiments on multiple datasets, including Cityscapes and human pose estimation datasets. Notably, they achieve an inference acceleration of 61% on Cityscapes while maintaining mIoU performance without re-training the model. This paper facilitates the deployment of transformer-based architectures on resource-constrained devices and in real-time applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to analyze really detailed images using powerful computers. But, these computers get stuck because they have to process too much information at once. To fix this, scientists found a way to combine similar pieces of information together, making it faster for the computer to work with. This helps make image analysis happen quicker and more efficiently. In this paper, researchers tested different ways to do this combining and saw big improvements in how fast they could process images. This is important because it means we can use these powerful computers on devices that aren’t as strong, like smartphones or tablets. |
Keywords
* Artificial intelligence * Image classification * Inference * Pose estimation * Semantic segmentation * Token * Transformer
