Brief description of what this page demonstrates.

Problem Statement

[Placeholder text for problem statement section. This would explain the challenge of automatically generating descriptive captions for images, the applications of image captioning technology, and how it bridges computer vision and natural language processing.]

\[\Large \mathcal{L}(\theta) = -\sum_{t=1}^{T} \log p(y_t | y_{1:t-1}, \mathbf{I}; \theta) \]

Image Preprocessing

[Placeholder text for image preprocessing section. This would cover techniques for preparing image data for the captioning model, including resizing, normalization, augmentation, and feature extraction using pre-trained CNNs.]

\[\Large I_{normalized} = \frac{I - \mu}{\sigma} \]

Text Preprocessing

[Placeholder text for text preprocessing section. This would explain tokenization, vocabulary building, special tokens like <START> and <END>, word embeddings, and handling variable-length sequences.]

\[\Large \text{embedding}(w) = E \cdot \text{onehot}(w) \in \mathbb{R}^d \]

Model Structure

[Placeholder text for model structure section. This would describe the encoder-decoder architecture, attention mechanisms, visual feature extraction, and how the model generates captions sequentially.]

\[\Large h_t = \text{LSTM}(h_{t-1}, [W_e y_{t-1}, \text{Attention}(h_{t-1}, \mathbf{I})]) \]

Interactive Demonstration

[Interactive demo placeholder]

Key Points