Summary of An Intermediate Fusion Vit Enables Efficient Text-image Alignment in Diffusion Models, by Zizhao Hu et al.
An Intermediate Fusion ViT Enables Efficient Text-Image Alignment in Diffusion Models
by Zizhao Hu, Shaochong Jia, Mohammad Rostami
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Diffusion models have been used for conditional data cross-modal generation tasks like text-to-image and text-to-video. However, state-of-the-art models struggle to align generated visual concepts with high-level semantics in languages like object count, spatial relationships, etc. This paper approaches this problem from a multimodal data fusion perspective, investigating how different fusion strategies impact vision-language alignment. The study discovers that intermediate fusion can boost text-to-image alignment with improved generation quality and reduce low-rank text-to-image attention calculations for faster training and inference. Experiments are performed on the MS-COCO dataset using text-to-image generation tasks and U-shaped ViT backbones. The intermediate fusion model achieves higher CLIP Scores, lower FID, with 20% reduced FLOPs and 50% increased training speed compared to a strong U-ViT baseline with early fusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers generate images from text descriptions. Right now, these computers are not very good at understanding what’s in the image they create. The researchers want to fix this problem by combining information from both the text and the image. They tested different ways of doing this and found that one method worked better than others. This new method made the generated images more accurate and allowed the computer to work faster and use less energy. The study used a popular dataset called MS-COCO and compared their results with other methods. |
Keywords
» Artificial intelligence » Alignment » Attention » Diffusion » Image generation » Inference » Semantics » Vit
