Summary of Elastic-detr: Making Image Resolution Learnable with Content-specific Network Prediction, by Daeun Seo et al.
Elastic-DETR: Making Image Resolution Learnable with Content-Specific Network Prediction
by Daeun Seo, Hoeseok Yang, Sihyeong Park, Hyungshin Kim
First submitted to arxiv on: 9 Dec 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 This paper introduces Elastic-DETR, a novel strategy for learnable multi-scale image resolution in object detectors like DETR. The method enables elastic utilization of multiple image resolutions by predicting an adaptive scale factor based on the image content. This is achieved through a compact scale prediction module (< 2 GFLOPs) and two loss functions: scale loss, which increases adaptiveness according to the image, and distribution loss, which determines the overall degree of scaling based on network performance. The paper demonstrates various models that exhibit varying trade-offs between accuracy and computational complexity, with a maximum accuracy gain of 3.5%p or 26% decrease in computation compared to MS-trained DN-DETR on the MS COCO dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes object detection more flexible by allowing the use of different image resolutions. It’s like having a special tool that can adapt to different situations, making it better at detecting objects or using less computer power. The tool uses a new way of predicting the best resolution based on what’s in the picture. This allows for more accurate results without using too much computer power. |
Keywords
» Artificial intelligence » Object detection
