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: A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization
🧠 Abstract: This work presents an in-depth analysis of the causal structure in the text encoder of text-to-image (T2I) diffusion models, highlighting its role in introducing information bias and loss. While prior research has mainly addressed these issues during the denoising stage, this study focuses on the underexplored contribution of text embeddings—particularly in multi-object generation scenarios. The authors investigate how text embeddings influence the final image output and why models often favor the first-mentioned object, leading to imbalanced representations. To mitigate this, they propose a training-free text embedding balance optimization method that improves information balance in Stable Diffusion by 125.42%. Additionally, a new automatic evaluation metric is introduced, offering a more accurate assessment of information loss with an 81% concordance rate with human evaluations. This metric better captures object presence and accuracy compared to existing measures like CLIP-based text-image similarity scores.
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: A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization
🧠 Abstract: This work presents an in-depth analysis of the causal structure in the text encoder of text-to-image (T2I) diffusion models, highlighting its role in introducing information bias and loss. While prior research has mainly addressed these issues during the denoising stage, this study focuses on the underexplored contribution of text embeddings—particularly in multi-object generation scenarios. The authors investigate how text embeddings influence the final image output and why models often favor the first-mentioned object, leading to imbalanced representations. To mitigate this, they propose a training-free text embedding balance optimization method that improves information balance in Stable Diffusion by 125.42%. Additionally, a new automatic evaluation metric is introduced, offering a more accurate assessment of information loss with an 81% concordance rate with human evaluations. This metric better captures object presence and accuracy compared to existing measures like CLIP-based text-image similarity scores.
From the Files app, scroll down to Internal storage, and tap on WhatsApp. Once you’re there, go to Media and then WhatsApp Stickers. Don’t be surprised if you find a large number of files in that folder—it holds your personal collection of stickers and every one you’ve ever received. Even the bad ones.Tap the three dots in the top right corner of your screen to Select all. If you want to trim the fat and grab only the best of the best, this is the perfect time to do so: choose the ones you want to export by long-pressing one file to activate selection mode, and then tapping on the rest. Once you’re done, hit the Share button (that “less than”-like symbol at the top of your screen). If you have a big collection—more than 500 stickers, for example—it’s possible that nothing will happen when you tap the Share button. Be patient—your phone’s just struggling with a heavy load.On the menu that pops from the bottom of the screen, choose Telegram, and then select the chat named Saved messages. This is a chat only you can see, and it will serve as your sticker bank. Unlike WhatsApp, Telegram doesn’t store your favorite stickers in a quick-access reservoir right beside the typing field, but you’ll be able to snatch them out of your Saved messages chat and forward them to any of your Telegram contacts. This also means you won’t have a quick way to save incoming stickers like you did on WhatsApp, so you’ll have to forward them from one chat to the other.
To pay the bills, Mr. Durov is issuing investors $1 billion to $1.5 billion of company debt, with the promise of discounted equity if the company eventually goes public, the people briefed on the plans said. He has also announced plans to start selling ads in public Telegram channels as soon as later this year, as well as offering other premium services for businesses and users.
