Image Captioning with Visual Attention

Learning Objectives

1. Create an Image Captioning Model: Learn how to build a model that generates descriptive captions for images.
2. Train and Predict Text Generation: Understand the process of training a model to generate text and evaluate its predictions.

Overview

Image captioning involves generating textual descriptions for images. The goal is to create a model that can output accurate and relevant captions for various images, akin to how a human might describe them.

For instance, given an image of people playing baseball, the model should generate a caption like "Some people are playing baseball."

Model Architecture

In this notebook, we will build an attention-based image captioning model inspired by the architecture from the paper "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention." This approach involves an encoder-decoder framework:

• Encoder: Processes the input image and generates an embedding representation.
• Decoder: Takes the image embedding and generates the textual description.

Notebook Features

• End-to-End Example: The notebook provides a complete walkthrough of building, training, and evaluating an image captioning model.
• Visual Attention Mechanism: Incorporates attention mechanisms to enhance caption generation by focusing on relevant parts of the image.

More Info:

https://github.com/GoogleCloudPlatform/asl-ml-immersion/blob/master/notebooks/multi_modal/solutions/image_captioning.ipynb