What is a good project combining computer vision and NLP?

A strong project combining computer vision and NLP is visual question answering (VQA), where a system answers text-based questions about images. This requires understanding both the visual content (via computer vision) and the linguistic meaning of the question (via NLP), then synthesizing a coherent answer. For example, given an image of a street scene and the question "What color is the traffic light?", the system must detect traffic lights in the image, analyze their state, and generate a text response. This project is practical because it mimics real-world applications like assistive technologies for the visually impaired or interactive educational tools.

To implement VQA, start by using a pre-trained vision model like ResNet or Vision Transformer (ViT) to extract image features. These features capture objects, colors, and spatial relationships. Simultaneously, process the question with an NLP model like BERT or GPT to extract its intent and key entities. Combine these outputs using a fusion layer (e.g., concatenation or attention mechanisms) to align visual and textual information. For training, use datasets like VQA v2.0 or GQA, which contain millions of image-question-answer triplets. A simple baseline could involve feeding fused features into a classifier to predict predefined answers, while advanced versions might use sequence-to-sequence models (e.g., T5) for open-ended responses.

Key challenges include handling ambiguous questions (e.g., “Is the vehicle moving?” when the image is static) and ensuring robustness to diverse image contexts. To improve performance, incorporate techniques like attention layers to focus on relevant image regions or fine-tune vision-language models like CLIP for better alignment. Developers can leverage frameworks like PyTorch or TensorFlow, with libraries such as HuggingFace Transformers for NLP components and OpenCV for image preprocessing. Testing with custom images and questions helps validate real-world usability, while metrics like accuracy (for closed answers) or BLEU score (for open-ended answers) quantify progress. This project offers a clear path from prototyping to deployment, making it both educational and scalable.