MJ-BENCH: A Multimodal AI Benchmark for Evaluating Text-to-Image Generation with Focus on Alignment, Safety, and Bias
What are DALLE-3 and Stable Diffusion designed for?
DALLE-3 and Stable Diffusion are both text-to-image generation models designed to translate text into visually coherent images. They use advanced AI technologies to create detailed and contextually accurate images based on textual prompts.
What common issues arise in text-to-image generation?
Text-to-image generation models face challenges such as misalignment between the image and text, hallucination of plausible entities not present in the instruction, bias, and unsafe or low-quality content generation. Addressing these issues is crucial for improving the reliability and safety of these models.
How do CLIP-based scoring models function?
CLIP-based scoring models evaluate the similarity between an image and a text description within the same embedding space. They use cosine similarity, a metric that determines the cosine of the angle between two vectors in a multidimensional space, to measure the similarity. The cosine similarity scale ranges from -1 to 1, where +1 indicates identical vectors, 0 means orthogonal vectors, and -1 suggests opposite vectors.