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Alright, let's get a bit more technical and see what's under the hood of TransCNN. As the name suggests, TransCNN integrates both transformer and CNN architectures. CNNs are fantastic at automatically and adaptively learning spatial hierarchies of features from 2D data, which makes them ideal for processing images or spectrograms derived from radar signals. On the other hand, transformers excel in capturing long-range dependencies in sequential data. By combining these two architectures, TransCNN can leverage both spatial feature extraction and temporal dependency modeling for improved gesture recognition accuracy. **Feature extraction** is a critical step in any machine learning task, and TransCNN's hybrid approach allows it to extract more relevant and informative features from radar data.
