The Swin(Shifted window) Transformer is a new machine-learning system that is currently based around the Transformer architecture.
The basic idea of this system is the same as it was with the original Transformer-based system or the Xception network: an encoder network takes an image, makes predictions and this prediction is run through a decoder network (which uses cross-entropy loss function) to output features.
GoogLeNet used an AlexNet-based encoder and a softmax output unit for the decoder layer. This lack of change in approach has changed with the SwinTransformer project.
It is a new library from Facebook AI Research (FAIR) which recently became public. The library allows to train neural networks to transform images into different kinds of outputs, such as an RGB image into a grayscale image, or a grayscale image into an edge map.
This technique can be used for example to remove distraction (e.g. remove heat signatures in thermal images) but also for adding/enhancing details (e.g. add edges to an image). One could use this approach also for example to enhance appearance of hand-written text or even to create new textures from other existing ones.
The post production lighting can be described as the final phase of the visual effects process. The Swin tool is used to put scene in a way that it emulates real-world lighting. It has become a popular choice of many VFX professionals and new market users as it provides global illumination using various algorithms like Radiosity, ambient occlusion and final gathering.
It is a series of image processing tools that were originally developed to aid in computer vision. Now, though, the tools have proven beneficial in a variety of ways—some you might expect, some you might not. It can be used in a variety of ways to enhance visual recognition and increase overall accuracy.
The Swin Transformer (ST) is both scalable and robust, which makes it easy to use in fields such as factory floor inspection, aerial mapping, and traffic monitoring. Additionally, its wide range of use cases means it’s useful as a tool in a variety of industries. This includes:
- Weed detection—Swin Transformer can be used for detecting weeds in fields for agriculture and farming
- Agricultural crop measurements—use the ST for determining crop height, leaf area index, and other metric in agriculture
- Civil engineering—use ST for bridge monitoring and other civil engineering applications
- Industrial process monitoring—monitor water quality or other industrial processes with ST
- Traffic monitoring—monitor traffic patterns for signs of accidents or other hazards with ST
Swin Transformer contains several layers, each consisting of several local processing operators coupled with weights for the computations. The layers are organized in two dimensions: spatial (x, y) and feature-wise (depth, intensity).
The first layer is called the coarse layer, which divides an image into coarse regions, whereas the second layer is called the fine layer, which further partitions the coarse regions into fine regions. The output of the Swin Transformer is represented by values between zero and one for each region indicating how likely it is that an object could be located at that region.
It has a number of unique features that make it suitable for computer vision applications like:
The model has a sparse representation of the input image allowing it to handle large amounts of data efficiently
It can process multiple input images simultaneously using parallel computations
It can handle a large variety of inputs: RGB color images as well
Swin Transformer is a computer vision model that uses a single neural network to learn how to recognize objects in images, videos, and audio. It is a neural network that can be trained to detect and classify objects in an image. Here are five unexpected ways that the Swin Transformer can be useful in computer vision:
- The Swin Transformer can be used to improve image recognition on your website.
- The Swin Transformer can be used to help you find out what’s on TV in your area with just a video of the screen showing the program.
- The Swin Transformer can be used by journalists to increase their objectivity and accuracy of news reports.
- The Swin Transformer can be used to improve the accuracy of medical diagnosis for cancer patients.
- The Swin Transformer has applications in automated video tracking
Here is a Swin Transformer repo that can help you
So, extending that to computer vision then, The Swin Transformer seems to have a lot of potential. It is especially useful in image classification and recognition. As such, I’m looking forward to seeing how it might be used in the future in computer vision applications. Hope you enjoyed reading this in MLDots.
