Summary of The Binary Quantized Neural Network For Dense Prediction Via Specially Designed Upsampling and Attention, by Xingyu Ding and Lianlei Shan and Guiqin Zhao and Meiqi Wu and Wenzhang Zhou and Wei Li
The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention
by Xingyu Ding, Lianlei Shan, Guiqin Zhao, Meiqi Wu, Wenzhang Zhou, Wei Li
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Deep learning-based information processing for dense prediction tasks like semantic segmentation and salient object detection is challenging due to quantization requirements. This paper proposes an effective upsampling method and efficient attention computation strategy based on binary neural networks (BNN) to overcome the challenges of maintaining high accuracy while reducing computational complexity. A simple and robust multi-branch parallel upsampling structure is designed to achieve high accuracy, followed by optimizing the attention method to reduce computational complexity by a factor of 100 times without sacrificing effectiveness. Experimental results on Cityscapes, KITTI road, and ECSSD demonstrate the success of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with deep learning-based information processing for tasks like image segmentation. The challenge is that these tasks require a lot of computation to process each tiny part of an image. Two main problems are fixed: one way to make predictions doesn’t work well, and another method to focus on important parts of the image is too slow. To fix this, the paper proposes two new ideas. First, it creates a special way to make predictions that works well and is fast. Second, it makes the attention method (which helps with segmentation) faster without losing its power. The results show that this approach really works well. |
Keywords
» Artificial intelligence » Attention » Deep learning » Image segmentation » Object detection » Quantization » Semantic segmentation
