Hi Mingming,
I Understand you are looking for more examples of sematic segmentation with vision transformers and their use-case.
Semantic segmentation is a computer vision task that involves labeling each pixel of an image with a class representing what is being depicted. Unlike image classification, which assigns a single label to the entire image, semantic segmentation provides a more granular understanding of the image's content.
Background Subtraction
- Semantic segmentation can be used to differentiate foreground objects from the background, which is a key aspect of background subtraction. This is particularly useful in video processing where you want to isolate moving objects from a static background.
Object Detection
- While object detection typically involves identifying bounding boxes around objects, semantic segmentation goes a step further by classifying each pixel within those bounding boxes. This allows for more precise localization of objects within an image.
Contour Detection
- Contour detection is related to edge detection but focuses on continuous edges or outlines of objects. Semantic segmentation can help in contour detection by providing accurate object shapes.
Image-to-Image Translation
- Image-to-image translation tasks, such as style transfer or generating photorealistic images from sketches, can leverage semantic segmentation to maintain consistency in the translation of different objects within an image.
These are some of the famous example which uses the semantic segmentation.
I hope it helps!
