I've recently delved into the world of computer vision and discovered an exciting vision-language model called LLaVA. This model has revolutionized the process of teaching a model to recognize specific features in an image.
Traditionally, training a model to recognize the color of a car in an image required a laborious process of training from scratch. However, with models like LLaVA, all you need to do is prompt it with a question like "What's the color of the car?" and voila! You get your answer, zero-shot style.
This approach mirrors the advancements we've seen in the field of natural language processing (NLP). Instead of training language models from scratch, researchers are now fine-tuning pre-trained models to suit their specific needs. Similarly, computer vision is heading in the same direction.
Imagine being able to extract valuable insights from images with a simple text prompt. And if you need to enhance the model's performance, a bit of fine-tuning can work wonders. In fact, my experiments have shown that fine-tuned models can even outperform those trained from scratch. It's like having the best of both worlds!
But here's the real game-changer: foundational models, thanks to their extensive training on massive datasets, possess a remarkable understanding of image representations. This means that you can fine-tune them with just a few examples, eliminating the need to collect thousands of images. In fact, they can even learn from a single example.
Development speed is another advantage of using text prompts to interact with images. With this approach, you can quickly create a computer vision prototype in seconds. It's fast, efficient, and it's revolutionizing the field.
So, are we moving towards a future where foundational models take the lead in computer vision, or is there still a place for training models from scratch? The answer to this question will shape the future of computer vision.
P.S. I'd like to shamelessly plug my open-source platform called Datasaurus. It harnesses the power of vision-language models to help engineers extract insights from images quickly. I wanted to share my thoughts and start a conversation about the future of computer vision. Let's talk!
