You intuitively know that poor quality images lead to poor object detection and vice-versa.

Image-Adaptive (IA)-YOLO improves object detection in adverse weather conditions using a hybrid task: image improvement combined with object detection.

• The paper, Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions, was published 3 days ago:

• The authors present an end-to-end hybrid data training task

• Each input image is adaptively enhanced to obtain better detection performance
• The hybrid architecture has a CNN-Parameter Predictor (PP) network that learns some appropriate Differentiable Image Processing (DIP) parameters to adaptivily enhance images for object detection, in a weakly supervised manner
• The DIP module consists of six differentiable filters with adjustable hyperparameters: Defog, White Balance(WB), Gamma, Contrast, Tone, and Sharpen.

• The CNN-PP network is trained using low-resolution images (256x256px) to speed the training
• CNN-PP parameters are used by the DIP module to improve the quality of the high-resolution images

• DIP processed (enhanced) images feed a YOLOv3 model for the object detection task
• IA-YOLO improves the baseline YOLO I by 0.89, 13.48 and 3.95 percent on VOC_norm_test, VOC_Dark_test and ExDark_test, respectively
📰 Paper: Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
• abs: https://t.co/ytKkcswB9b
• pdf: https://t.co/gPFexKL22Z
• Repo: https://t.co/sVF4Xz6ddz

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6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
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https://t.co/qyl4Bt1i5x


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32 unique background colors
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7 unique z-indices

https://t.co/w7oNG5KUkJ


The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.

The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.