October 27, 2023
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!
Aishwarya Nair, dubbed "Lotto Lorekeeper" at LottoRanker, harnesses her meticulous research skills and cultural depth from Kerala, India, to shed light on global lottery phenomena. Equipped with a keen sense of detail and a penchant for data, she delves deep into the lottery world, unveiling hidden gems and trending patterns.