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Review — Learning to Resize Images for Computer Vision Tasks (Image Classification & Image Quality Assessment)
Learned Resizer Instead of Conventional Resizers, Joint Learning of Image Resizer and Recognition Model
In this story, Learning to Resize Images for Computer Vision Tasks, (Learned Resizer), by Google Research, is reviewed.
- Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g. 224×224), before inputting into the CNN for both training and inference.
- An off-the-shelf image resizers such as bilinear and bicubic are used.
- In this paper, the typical linear resizer is replaced with learned resizers that can substantially improve performance.
This is a paper in 2021 arXiv. (Sik-Ho Tsang @ Medium)
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