What does "Local Self-attention" mean?

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Local self-attention is a technique used in machine learning to focus on important parts of data while ignoring less relevant information. This method helps a model to pay more attention to nearby items in a dataset, which can improve its ability to understand and process that data.

In tasks like analyzing images or 3D point clouds, local self-attention allows the model to look closely at small groups of points or pixels. By doing this, the model can better identify patterns or features that are crucial for making accurate predictions.

The main idea is that instead of considering all data points at once, local self-attention hones in on smaller sections. This approach makes it easier and faster for the model to learn and make decisions based on the relevant information available in its immediate surroundings.