Visual Question Answering: Teaching Computers to See and Understand

Imagine you have a picture of a park and you ask your computer, "How many trees are there?". An ideal answer wouldn't just count the trees, it would understand the concept of "how many." This is the goal of Visual Question Answering (VQA), a field of computer science that enables computers to answer questions about images.

VQA combines computer vision and natural language processing (NLP) to bridge the gap between visual and linguistic understanding. Here's a breakdown of how it works:

1. Extracting Features:

• From the image, features like shapes, colors, and object recognition are extracted using pre-trained Convolutional Neural Networks (CNNs).
• The question is analyzed using methods like Bag-of-Words (BOW) or Long Short-Term Memory (LSTM) encoders to understand its meaning.

1. Combining Features:

• Different techniques are used to combine the extracted features from the image and the question. This creates a unified representation that the model can use to generate an answer.

1. Answer Generation:

• Often, the problem is modeled as a classification task. The model considers the combined features and outputs the most likely answer from a set of possibilities.

VQA models come in various flavors, each with its strengths:

• Pix2Struct: This deep learning model leverages image-to-text translation. It analyzes the image, breaks it down into parts, and then generates a description that considers the question.
• BLIP-2 (Bootstrapping Language-Image Pre-training): This efficient model focuses on using pre-trained components. It combines a frozen image encoder, a large language model (LLM), and a lightweight neural network to answer questions.
• GPT-4 with Vision: This groundbreaking model processes information jointly. It analyzes the image and question together to create a richer understanding before generating an answer.

Evaluating VQA models requires special metrics because the answers are open-ended. Some common metrics include:

• WUPS measure: Estimates the semantic similarity between the answer and the ground truth.
• METEOR and BLEU measure: Inspired by machine translation, these metrics assess the quality of generated answers based on precision, recall, and similarity to reference answers.

Training and evaluating VQA models rely on large datasets of images and corresponding questions and answers. Some popular datasets include:

• COCO-QA: This dataset contains images from the COCO dataset with automatically generated questions based on image captions.
• DAQUAR: This dataset focuses on indoor scenes with multiple question-answer pairs per image.
• Visual QA dataset: This large dataset incorporates real images and abstract cartoons with multiple questions and answer choices per image.

VQA has the potential to revolutionize how computers interact with visual content. It has applications in image retrieval, education, and creating more interactive experiences with visual media. As VQA models continue to develop, we can expect even more sophisticated ways for computers to understand and reason about the visual world.

Source : https://arxiv.org/pdf/1906.00067