Summary of Improving Image Coding For Machines Through Optimizing Encoder Via Auxiliary Loss, by Kei Iino et al.
Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss
by Kei Iino, Shunsuke Akamatsu, Hiroshi Watanabe, Shohei Enomoto, Akira Sakamoto, Takeharu Eda
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Machine learning researchers have been developing techniques for compressing images to enable efficient processing by machines. This field, known as Image Coding for Machines (ICM), focuses on recognizing and compressing information essential for machine recognition tasks. Two primary approaches in learned ICM include optimizing the compression model based on task loss and using Region of Interest (ROI) based bit allocation. While these methods enhance the encoder’s recognition capabilities, they can become challenging when working with deep recognition models or involve additional overhead during evaluation. A novel training method is proposed to improve the recognition capability and rate-distortion performance of learned ICM models by applying auxiliary losses to the encoder. This approach achieves significant improvements in object detection (27.7%) and semantic segmentation tasks (20.3%), outperforming conventional training methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping computers understand pictures better! Researchers are working on a way to shrink images so machines can process them quickly. This is called “Image Coding for Machines” or ICM. There are two main ways to do this: one that makes the compression model work better, and another that focuses on important parts of the image. These methods help computers recognize things in pictures. But they can be tricky when working with really deep computer models or take a long time. Scientists have come up with a new way to train these machines to make them even better at recognizing what’s in the pictures. This new method does a great job (27.7% and 20.3%)! |
Keywords
» Artificial intelligence » Encoder » Machine learning » Object detection » Semantic segmentation
