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.
🌀 Abstract: Recent advancements in text-conditioned image generation, particularly through latent diffusion models, have achieved significant progress. However, as text complexity increases, these models often struggle to accurately capture the semantics of prompts, and existing tools like CLIP frequently fail to detect these misalignments.
This presentation introduces a Decompositional-Alignment-Score, which breaks down complex prompts into individual assertions and evaluates their alignment with generated images using a visual question answering (VQA) model. These scores are then combined to produce a final alignment score. Experimental results show this method aligns better with human judgments compared to traditional CLIP and BLIP scores. Moreover, it enables an iterative process that improves text-to-image alignment by 8.7% over previous methods.
This approach not only enhances evaluation but also provides actionable feedback for generating more accurate images from complex textual inputs.
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.
🌀 Abstract: Recent advancements in text-conditioned image generation, particularly through latent diffusion models, have achieved significant progress. However, as text complexity increases, these models often struggle to accurately capture the semantics of prompts, and existing tools like CLIP frequently fail to detect these misalignments.
This presentation introduces a Decompositional-Alignment-Score, which breaks down complex prompts into individual assertions and evaluates their alignment with generated images using a visual question answering (VQA) model. These scores are then combined to produce a final alignment score. Experimental results show this method aligns better with human judgments compared to traditional CLIP and BLIP scores. Moreover, it enables an iterative process that improves text-to-image alignment by 8.7% over previous methods.
This approach not only enhances evaluation but also provides actionable feedback for generating more accurate images from complex textual inputs.
Most people buy Bitcoin via exchanges, such as Coinbase. Exchanges allow you to buy, sell and hold cryptocurrency, and setting up an account is similar to opening a brokerage account—you’ll need to verify your identity and provide some kind of funding source, such as a bank account or debit card. Major exchanges include Coinbase, Kraken, and Gemini. You can also buy Bitcoin at a broker like Robinhood. Regardless of where you buy your Bitcoin, you’ll need a digital wallet in which to store it. This might be what’s called a hot wallet or a cold wallet. A hot wallet (also called an online wallet) is stored by an exchange or a provider in the cloud. Providers of online wallets include Exodus, Electrum and Mycelium. A cold wallet (or mobile wallet) is an offline device used to store Bitcoin and is not connected to the Internet. Some mobile wallet options include Trezor and Ledger.
Should I buy bitcoin?
“To the extent it is used I fear it’s often for illicit finance. It’s an extremely inefficient way of conducting transactions, and the amount of energy that’s consumed in processing those transactions is staggering,” the former Fed chairwoman said. Yellen’s comments have been cited as a reason for bitcoin’s recent losses. However, Yellen’s assessment of bitcoin as a inefficient medium of exchange is an important point and one that has already been raised in the past by bitcoin bulls. Using a volatile asset in exchange for goods and services makes little sense if the asset can tumble 10% in a day, or surge 80% over the course of a two months as bitcoin has done in 2021, critics argue. To put a finer point on it, over the past 12 months bitcoin has registered 8 corrections, defined as a decline from a recent peak of at least 10% but not more than 20%, and two bear markets, which are defined as falls of 20% or more, according to Dow Jones Market Data.
