Summary of Efficient Transformer Encoders For Mask2former-style Models, by Manyi Yao et al.
Efficient Transformer Encoders for Mask2Former-style models
by Manyi Yao, Abhishek Aich, Yumin Suh, Amit Roy-Chowdhury, Christian Shelton, Manmohan Chandraker
First submitted to arxiv on: 23 Apr 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 The vision transformer-based models bring significant improvements for image segmentation tasks. However, their use of computational resources can be taxing on deployed devices. To overcome this challenge, researchers introduced ECO-M2F, a strategy that self-selects the number of hidden layers in the encoder based on the input image. This approach is achieved through a three-step recipe: training the parent architecture to enable early exiting from the encoder, creating a derived dataset of the ideal number of encoder layers required for each training example, and using this dataset to train a gating network that predicts the number of encoder layers to be used, conditioned on the input image. The proposed approach reduces expected encoder computational cost while maintaining performance, adapts to various user compute resources, is flexible in architecture configurations, and can be extended beyond the segmentation task to object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make image segmentation models work better on devices that don’t have a lot of power. They created a special kind of model called ECO-M2F that can choose how many layers it uses based on the picture it’s looking at. This helps the model use less power while still getting good results. The researchers came up with a three-step plan to make this happen: first, they trained the main part of the model to stop using as many layers when possible; second, they made a special dataset that shows which layer counts work best for each picture; and third, they used this dataset to train another part of the model that can decide how many layers to use based on the picture. This new approach works well, uses less power, and can be used with other tasks like object detection. |
Keywords
» Artificial intelligence » Encoder » Image segmentation » Object detection » Vision transformer
