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Image Captioning, Transformer Mode On
- The CPTR architecture combines the encoder part of the ViT model with the decoder part of the original Transformer model for image captioning.
- CPTR utilizes ViT Encoder to encode input images into a tensor representation for Transformer Decoder to generate captions.
- The CPTR model includes parameters for image size, caption length, embed dimension, patch size, and the number of encoder and decoder blocks.
- Components like Patcher, LearnableEmbedding, EncoderBlock, SinusoidalEmbedding, and DecoderBlock are implemented for the CPTR model.
- Encoder part processes image patches and positional embedding, while the Decoder part converts words into vectors and includes self-attention and cross-attention layers.
- Triangular matrices are used to create masks for the self-attention mechanism in the decoder to prevent attending to subsequent words.
- The CPTR architecture is implemented by assembling the ViT Encoder and Transformer Decoder components, enabling training on image captioning datasets.
- Alternative simpler implementations utilizing PyTorch's nn.TransformerEncoderLayer and nn.TransformerDecoderLayer are also discussed for Encoder and Decoder.
- The CPTR model is designed for autoregressive image captioning, seamlessly integrating encoder and decoder components for context-aware caption generation.
- The implementation details and flow of tensors demonstrate the functionality and processing steps of each component in the CPTR model.
- The article provides insights into the theory and implementation of the CaPtion TransformeR architecture for image captioning tasks.
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