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: This presentation introduces advancements in addressing compositional challenges in text-to-image (T2I) generation models. Current diffusion models often struggle to associate attributes accurately with the intended objects based on text prompts. To address this, a new Edge Prediction Vision Transformer (EPViT) is introduced for improved image-text alignment evaluation. Additionally, the proposed Focused Cross-Attention (FCA) mechanism uses syntactic constraints from input sentences to enhance visual attention maps. DisCLIP embeddings further disentangle multimodal embeddings, improving attribute-object alignment. These innovations integrate seamlessly into state-of-the-art diffusion models, enhancing T2I generation quality without additional model training.
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: This presentation introduces advancements in addressing compositional challenges in text-to-image (T2I) generation models. Current diffusion models often struggle to associate attributes accurately with the intended objects based on text prompts. To address this, a new Edge Prediction Vision Transformer (EPViT) is introduced for improved image-text alignment evaluation. Additionally, the proposed Focused Cross-Attention (FCA) mechanism uses syntactic constraints from input sentences to enhance visual attention maps. DisCLIP embeddings further disentangle multimodal embeddings, improving attribute-object alignment. These innovations integrate seamlessly into state-of-the-art diffusion models, enhancing T2I generation quality without additional model training.
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.
The STAR Market, as is implied by the name, is heavily geared toward smaller innovative tech companies, in particular those engaged in strategically important fields, such as biopharmaceuticals, 5G technology, semiconductors, and new energy. The STAR Market currently has 340 listed securities. The STAR Market is seen as important for China’s high-tech and emerging industries, providing a space for smaller companies to raise capital in China. This is especially significant for technology companies that may be viewed with suspicion on overseas stock exchanges.
