Aragon has been covering computer vision for seven years. With the Advent of #GenerativeAI, a new term has emerged relative to Gen AI, Image, and Video: #LargeVisionModels
Learn more ⬇️ bit.ly/4febwml
🌌 Understanding the quality of AI-generated images from text just got a deep dive. Sebastian Hartwig, Dominik Engel, & team present a study on evaluating text-to-image synthesis. 📊
Evaluation Complexity: Beyond pixels to human judgment. 🧠
New Taxonomy: Classifying metrics for better insights. 📚
Optimizing Creativity: Making AI art truer to our words. ✨
A must-read for #GenAI enthusiasts eager to explore the intersection of art, language, and AI. 🚀
#LargeVisionModels#AIResearch@hopprai
Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics
This survey provides an overview of the current state-of-the-art (SOTA) evaluation metrics for #TextToImage synthesis.
buff.ly/3TtKgpA
Domain-Specific LVMs can adapt to any environment, boosting operational efficiency. Join David Park and Quinn Killough tomorrow to see real-life case studies and practical applications!
Register: bit.ly/4aIkvtt#LargeVisionModels#ComputerVision
For businesses with a large, proprietary set of image or video data within a specialized domain, #LargeVisionModels offer a recipe to unlock the tremendous value latent in that data.
Learn more: bit.ly/46HMGFJ#ComputerVision
I see this argument all the time from tech people: Building gargantuan AI models may be computationally, environmentally, and financially costly. But if those models then go on to solve cancer, isn't that on balance better for the world?
NOOOO.
A thread.