Summary of Parformer: a Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding, by Novendra Setyawan et al.
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding
by Novendra Setyawan, Ghufron Wahyu Kurniawan, Chi-Chia Sun, Jun-Wei Hsieh, Jing-Ming Guo, Wen-Kai Kuo
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Medium Difficulty summary: This paper introduces ParFormer, a novel vision transformer that tackles the issue of high computational redundancy in deep computer vision models. By combining convolutional and attention mechanisms, ParFormer improves feature extraction efficiency while preserving essential information during down-sampling. The SCAPE module further reduces redundant computation, making it suitable for resource-constrained environments like edge devices. Experimental results on ImageNet-1K show that ParFormer-T achieves 78.9% Top-1 accuracy with high throughput, outperforming other small models like MobileViT-S and FasterNet-T2. For edge device deployment, ParFormer-T excels with a throughput of 278.1 images/sec, making it ideal for real-time applications. The larger variant, ParFormer-L, reaches 83.5% Top-1 accuracy, offering a balanced trade-off between accuracy and efficiency. Additionally, ParFormer-M achieves state-of-the-art results in COCO object detection and instance segmentation while maintaining high efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper talks about making computer vision models work better on devices with limited resources, like smartphones or smart cameras. The new model, called ParFormer, is more efficient than other models that use a lot of energy and can process images quickly. It’s like having a super-powerful camera in your phone! The researchers tested ParFormer on a big dataset and it did really well, even better than some other models that are already popular. They also showed that it can be used for tasks like object detection and segmentation, which is useful for things like self-driving cars or surveillance cameras. |
Keywords
* Artificial intelligence * Attention * Feature extraction * Instance segmentation * Object detection * Vision transformer
