Imagine you have an image and want to know what’s inside each little square of that picture (like a puzzle). That’s called semantic segmentation! Instead of using traditional convolutional neural networks, we can use something called Dense Vision Transformers (DPT) which is like a fancy way of saying “we’re gonna transform the image and then make it dense with information”.

So how does DPT work? First, we take our input image and feed it into a vision transformer (ViT), which is basically an algorithm that can process images using attention mechanisms. This ViT will give us some cool features to play around with! Then, we assemble these features from different stages of the ViT into an image-like representation at various resolutions.

Next, we combine all those representations together using a convolutional decoder (which is like a fancy way of saying “we’re gonna decode this information and make it more dense”). This will give us our final output: a segmented image with each little square labeled as either background or one of the 21 classes in COCO dataset.

DPT has some cool features that set it apart from traditional convolutional neural networks for semantic segmentation. First, DPT has a global receptive field at every stage which means it can process representations at a constant and relatively high resolution. This allows us to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks (which is like saying “we’re gonna make our predictions more accurate and consistent”).

In terms of results, DPT has set new state-of-the-art on ADE20K with 49.02% mIoU (mean Intersection over Union) which means it can accurately segment images better than other methods out there! And the best part? It’s not just limited to semantic segmentation DPT has also been used for depth estimation, where it achieved an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network.

A more relatable explanation of how DPT works for semantic segmentation using images. Who knew that transformers could be so useful?