Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.


This Week's Presentation:

🔹 Title: Correcting Diffusion Generation through Resampling


🔸 Presenter: Ali Aghayari

🌀 Abstract:
This paper addresses distributional discrepancies in diffusion models, which cause missing objects in text-to-image generation and reduced image quality. Existing methods overlook this root issue, leading to
suboptimal results. The authors propose a particle filtering framework that uses real images and a pre-trained object detector to measure and correct these discrepancies through resampling. Their approach improves object occurrence by 5% and FID by 1.0 on MS-COCO, outperforming previous methods in generating more accurate and higher-quality images.


📄 Papers: Correcting Diffusion Generation through Resampling


Session Details:
- 📅 Date: Tuesday
- 🕒 Time: 5:30 - 6:30 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban


@Ai_Events

Ai Events️

Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.


This Week's Presentation:

🔹 Title: Correcting Diffusion Generation through Resampling


🔸 Presenter: Ali Aghayari

🌀 Abstract:
This paper addresses distributional discrepancies in diffusion models, which cause missing objects in text-to-image generation and reduced image quality. Existing methods overlook this root issue, leading to
suboptimal results. The authors propose a particle filtering framework that uses real images and a pre-trained object detector to measure and correct these discrepancies through resampling. Their approach improves object occurrence by 5% and FID by 1.0 on MS-COCO, outperforming previous methods in generating more accurate and higher-quality images.


📄 Papers: Correcting Diffusion Generation through Resampling


Session Details:
- 📅 Date: Tuesday
- 🕒 Time: 5:30 - 6:30 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban


@Ai_Events

hottg.com/AI_Events/1691

2.6K viewsحمید محمودآبادی, Feb 4 at 05:03