The article discusses advancements in image captioning through the CPTR model developed by Liu et al. in 2021. It highlights the evolution from earlier models like 'Show and Tell' to the more sophisticated CPTR architecture. This new model utilizes a Vision Transformer as its encoder and a Transformer for the decoder, significantly advancing the image captioning technique by enhancing the neural network's ability to generate relevant captions. While the focus is on architecture, the author suggests familiarity with underlying theories of ViT and Transformer models for a comprehensive understanding.
The CPTR architecture integrates Vision Transformer and Transformer models, enhancing the encoder-decoder structure crucial for advancing image captioning techniques.
While the CPTR maintains the encoder-decoder format seen earlier, its innovation comes from utilizing transformer models, providing a more sophisticated approach to image caption generation.
The earlier model utilized GoogLeNet and LSTM, but the CPTR introduces a more refined architecture that promises to improve image captioning outcomes through its full transformer system.
Understanding CPTR’s theory requires familiarity with ViT and Transformer models; thus, reviewing foundational concepts is essential before diving into this advanced architecture.
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