Dense Prediction Transformers (DPT) are a deep learning architecture utilized in computer vision applications like semantic segmentation, object detection, and instance segmentation. The fundamental concept of DPT involves generating dense image labels through the incorporation of global and local context information. An extensive explanation of Ambrosinus Toolkit and the integration of the AI Diffuse Models in the AEC creative design processes has been discussed in the official research document within the Coding Architecture book chapter 😉. DPT represents an advanced architecture in the field of visual processing, which breaks away from traditional convolutional networks (CNN) to embrace transformers, known for their effectiveness in processing sequential data. These models are particularly well suited for dense prediction tasks, such as monocular depth estimation and semantic segmentation, due to their ability to handle high-resolution representations and capture global contexts within images. Combining token representations at different resolutions and using a convolutional decoder to synthesize full-resolution predictions allows DPTs to provide more precise and detailed results. This technology has already been shown to significantly improve performance in various computer vision tasks, setting new benchmarks in the field. Imagine you need to label objects in an image. Here’s how they work:

Convolutional Networks (CNN): CNNs are like artists who specialize in analyzing specific parts of the image. They scan the image with filters to detect edges, textures and shapes. Example: If they see a car, they might say: “There’s a car!”.

Dense Prediction Transformers (DPT): DPTs are like poetry writers who understand the image as a whole. They look at the image and create a detailed map of what they see. Example: If they see a road, they might say: “This is an asphalt road, with trees on the sides and a parked car.”

In short, CNNs focus on specific parts, while DPTs understand the image as a whole.