Soft Split and Soft Composition: A Guide to Understanding
The FuseFormer architecture is a recently developed model that has caught the interest of the machine learning community. It has shown exceptional results in the task of image segmentation, which is used in many fields such as medical imaging, robotics, and self-driving cars. One of the unique aspects of the FuseFormer architecture is the use of Soft Split and Soft Composition operations, which we'll be discussing in this article.
What is Soft Split and Soft Composition?
Soft Split and Soft Composition are image processing techniques that are used in machine learning models to identify objects present in an image. These techniques are exclusively used in the FuseFormer architecture, ensuring that it has come up with exceptional results.
Soft Split refers to the process of separating an image into overlapped patches softly. These patches are disassembled and reapplied to the same image, allowing easy and convenient processing of data. Soft Composition is the process of reconstructing the image from the split or encoded features. In this process, the separated patches, which might contain different information, are put together or composed. The information is accumulated through a series of overlapping patches to make the initial or full image.
The Soft Split operation can be used to create localized features within an image. These localized features can be used to identify smaller objects within an image, such as a person's face or the wheels of a car. It's similar to the idea of dividing a larger problem into smaller pieces to make it easier to solve. Soft Composition, on the other hand, enables the creation of global features that allow for a more holistic view of the image. It aggregates the small pieces or patches while still keeping finer details.
How does Soft Split and Soft Composition Work?
Soft Split and Soft Composition can be seen as two sides of the same coin. The process involves the creation of local features that can be used to form an image. Soft Split divides the image into smaller parts or patches that allow for more data processing, and Soft Composition combines these patches to re-form the initial image.
The disassembling and reassembling of the image are done using unfolding and folding operations, respectively. The unfold operation separates patches in every direction, namely up, down, left, and right. It ensures that each pixel present in the patch is multiplied across the space dimensions, and it's features are reduced across the channel dimensions. The resulting features of the unfold operation get processed using convolutional operations (learned parameters).
After processing the patches' features, the fold operation is used to bring the patches back together and, in turn, reconstruct the image. In the fold operation, the patches are stacked up in a manner that allows overlapping or recurring features. The features at each location of the overlapping patches are then summed up, and the resulting value gets used to reconstruct the image.
The value for how much overlap we want in the Soft Split operation (which is defined by patch size $k$ being larger than patch stride $s$) generally depends on the type of image data being processed. It can be set such that the resulting patches overlap significantly or not at all.
Advantages of Soft Split and Soft Composition
1. Ability to identify small features: The Soft Split operation is useful in identifying small features in an image. This process is essential because most objects are small and need to be identified to get a better view of the entire image.
2. Fine details: The Soft Split and Soft Composition operations take into account finer details of an image, allowing for more precise predictions. Adding overlapping patches in Soft Composition hampers any value loss by summation and results in a fine-grained image.
3. Reduction in computation cost: Dividing the image into smaller patches using Soft Split operation implies that fewer calculations are performed. Reduced computations mean a lighter model, allowing for faster computation times.
Soft Split and Soft Composition operations are image processing techniques that have been implemented in the FuseFormer architecture to achieve remarkable results. The two methods allow for a precise and holistic view of an image simultaneously. Soft Split operation supports the creation of localized features, whereas Soft Composition methods create global features, including fine details of the object, that are then used to represent the initial image.
Soft Split and Soft Composition operations have demonstrated superiority in detecting and identifying objects present in an image, leading to remarkable advancements in fields such as self-driving cars, medical imaging, and robotics. With the advantages they provide, there's no doubt that we'll be seeing more models incorporating Soft Split and Soft Composition operations in the future.
