The Channel-wise Cross Fusion Transformer, also known as the CCT module, is an important component used in the UCTransNet architecture for semantic segmentation.
What is UCTransNet?
UCTransNet is a deep learning architecture used for semantic segmentation, which is a task in computer vision that involves grouping different parts of an image into specific categories. For example, a semantic segmentation model can identify and label objects in an image like cars, pedestrians, or buildings. This architecture uses the Channel-wise Cross Fusion Transformer to integrate multi-scale encoder features, enabling better performance for the semantic segmentation task.
What is the Channel-wise Cross Fusion Transformer?
The Channel-wise Cross Fusion Transformer (CCT) module is composed of three main steps: multi-scale feature embedding, multi-head channel-wise cross attention, and a Multi-Layer Perceptron (MLP). Let's break down these steps further to understand how they work together.
Multi-scale feature embedding
The input to the CCT module is a set of multi-scale features, which have been extracted from different layers of a convolutional neural network (CNN). Initially, these features are embedded into a higher-dimensional space using a linear transformation that preserves their semantic information. This embedding step facilitates the integration of multi-scale features with different resolutions and levels of detail.
Multi-head channel-wise cross attention
The next step involves multi-head channel-wise cross attention, which is a modification of the original Transformer architecture. The Transformer is a popular deep learning model used for natural language processing, but it has also shown strong performance in computer vision tasks like object detection and segmentation. Channel-wise cross attention refers to the process of computing attention weights between different channels within a feature map, instead of between different positions in a sequence like in the original Transformer. This process considers the relationships between all channels, making it well-suited for integrating multi-scale features with varying channel dimensions.
Multi-Layer Perceptron
The final step is the Multi-Layer Perceptron (MLP), which takes the channel-wise attention output and transforms it into a new feature set. This MLP consists of several fully connected layers, which allow for further processing of the attention output to produce features that are compatible with the downstream classification or segmentation task.
Why is the CCT module important for semantic segmentation?
The Channel-wise Cross Fusion Transformer enables better performance in semantic segmentation tasks by effectively fusing information from different layers of the CNN and capturing long-range dependencies between different parts of the image. The multi-head channel-wise cross attention mechanism allows for flexible integration of the multi-scale features with varying dimensions, while the MLP provides the capacity to transform these features into a more discriminative feature set for the final segmentation step. Using the CCT module has shown state-of-the-art performance on a variety of benchmarks in the computer vision community.
The Channel-wise Cross Fusion Transformer is a module used in the UCTransNet architecture for semantic segmentation. It involves multi-scale feature embedding, multi-head channel-wise cross attention, and a Multi-Layer Perceptron. The CCT enables better performance in semantic segmentation tasks by capturing long-range dependencies and fusing information from different layers of the CNN. Using the CCT module has shown state-of-the-art performance on a variety of benchmarks in the computer vision community.
