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๐Ÿ’  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.

โœ… This Week's Presentation:

๐Ÿ”น Title: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


๐Ÿ”ธ Presenter: Amir Kasaei

๐ŸŒ€ Abstract:

This paper explores the use of Chain-of-Thought (CoT) reasoning to improve autoregressive image generation, an area not widely studied. The authors propose three techniques: scaling computation for verification, aligning preferences with Direct Preference Optimization (DPO), and integrating these methods for enhanced performance. They introduce two new reward models, PARM and PARM++, which adaptively assess and correct image generations. Their approach improves the Show-o model, achieving a +24% gain on the GenEval benchmark and surpassing Stable Diffusion 3 by +15%.


๐Ÿ“„ Papers: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


Session Details:
- ๐Ÿ“… Date: Wednesday
- ๐Ÿ•’ Time: 2:15 - 3:15 PM
- ๐ŸŒ Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! โœŒ๏ธ



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๐Ÿ’  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.

โœ… This Week's Presentation:

๐Ÿ”น Title: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


๐Ÿ”ธ Presenter: Amir Kasaei

๐ŸŒ€ Abstract:

This paper explores the use of Chain-of-Thought (CoT) reasoning to improve autoregressive image generation, an area not widely studied. The authors propose three techniques: scaling computation for verification, aligning preferences with Direct Preference Optimization (DPO), and integrating these methods for enhanced performance. They introduce two new reward models, PARM and PARM++, which adaptively assess and correct image generations. Their approach improves the Show-o model, achieving a +24% gain on the GenEval benchmark and surpassing Stable Diffusion 3 by +15%.


๐Ÿ“„ Papers: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


Session Details:
- ๐Ÿ“… Date: Wednesday
- ๐Ÿ•’ Time: 2:15 - 3:15 PM
- ๐ŸŒ Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! โœŒ๏ธ

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