Transformers have become the leading framework in Natural Language Processing due to their self-attention mechanisms which allow for efficient processing and scalability. Unlike traditional architectures such as RNNs or LSTMs, transformers can handle varying input lengths without losing context. However, in computer vision, convolutional neural networks (CNNs) continue to excel. Integrating transformers in vision tasks has proven challenging, primarily due to the self-attention mechanism's quadratic time complexity, which poses scalability issues. Despite ongoing research, CNNs remain the dominant architecture in visual processing tasks.
Transformers are the leading NLP models due to their self-attention mechanism, which improves computational efficiency, scalability, and performance on various linguistic tasks.
The key benefit of transformers is their ability to process sequences of any length without context loss, a major enhancement over traditional RNNs and LSTMs.
Despite promising research on integrating transformers into computer vision, CNNs still dominate due to the quadratic time complexity of the self-attention mechanism.
The challenge of applying transformers to computer vision is highlighted by their O(n²) complexity, which affects performance with longer sequences, unlike NLP applications.
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