Have you ever wondered how multimodal language models like GPT process visual and textual information? Visualizing attention in these models can help us better understand how they work. In computer vision, techniques like Grad-CAM are used to generate heatmap-like maps that show what the model is focusing on. But can we apply similar techniques to multimodal language models?
One approach could be to use attention rollout, attention times gradient, or integrated gradients on the vision encoder. These methods can help us understand what the model is ‘seeing’ and how it’s using that information to generate text. But are there any open-source tools or examples that can help us get started?
If you’ve worked on visualizing attention in multimodal language models, I’d love to hear about your experience. What approaches have you found most effective, and are there any tools or libraries that you recommend?
Understanding how these models work can help us build more accurate and informative AI systems. So let’s dive in and explore the possibilities!