Enhancing automatic image cropping models with advanced adversarial techniques
Image cropping is an essential task in many contexts, right from social media and e-commerce to advanced computer vision applications. Cropping helps maintain image quality by avoiding unnecessary resizing, which can degrade the image and consume computational resources. It is also useful when an image needs to conform to a predetermined aspect ratio, such as in thumbnails. Over the past decade, engineers around the world have developed various machine learning (ML) models to automatically crop images. These models aim to crop an input image in a way that preserves its most relevant parts. However, ...
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