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Apple Releases 4M-21: A Very Effective Multimodal AI Model that Solves Tens of Tasks and Modalities

Large language models (LLMs) have made significant strides in handling multiple modalities and tasks, but they still need to improve their ability to process diverse inputs and perform a wide range of tasks effectively. The primary challenge lies in developing a single neural network capable of handling a broad spectrum of tasks and modalities while…

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TiTok: An Innovative AI Method for Tokenizing Images into 1D Latent Sequences

In recent years, image generation has made significant progress due to advancements in both transformers and diffusion models. Similar to trends in generative language models, many modern image generation models now use standard image tokenizers and de-tokenizers. Despite showing great success in image generation, image tokenizers encounter fundamental limitations due to the way they are…

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NVIDIA’s Autoguidance: Improving Image Quality and Variation in Diffusion Models

Improving image quality and variation in diffusion models without compromising alignment with given conditions, such as class labels or text prompts, is a significant challenge. Current methods often enhance image quality at the expense of diversity, limiting their applicability in various real-world scenarios such as medical diagnosis and autonomous driving, where both high quality and…

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Beyond High-Level Features: Dense Connector Boosts Multimodal Large Language Models MLLMs with Multi-Layer Visual Integration

Multimodal Large Language Models (MLLMs) represent an advanced field in artificial intelligence where models integrate visual and textual information to understand and generate responses. These models have evolved from large language models (LLMs) that excelled in text comprehension and generation to now also processing and understanding visual data, enhancing their overall capabilities significantly. The main…

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CinePile: A Novel Dataset and Benchmark Specifically Designed for Authentic Long-Form Video Understanding

Video understanding is one of the evolving areas of research in artificial intelligence (AI), focusing on enabling machines to comprehend and analyze visual content. Tasks like recognizing objects, understanding human actions, and interpreting events within a video come under this domain. Advancements in this domain find crucial applications in autonomous driving, surveillance, and entertainment industries.…

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Advancements in Knowledge Distillation and Multi-Teacher Learning: Introducing AM-RADIO Framework

Knowledge Distillation has gained popularity for transferring the expertise of a “teacher” model to a smaller “student” model. Initially, an iterative learning process involving a high-capacity model is employed.  The student, with equal or greater capacity, is trained with extensive augmentation. Subsequently, the trained student expands the dataset through pseudo-labeling new data. Notably, the student…

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Stylus: An AI Tool that Automatically Finds and Adds the Best Adapters (LoRAs, Textual Inversions, Hypernetworks) to Stable Diffusion based on Your Prompt

Adopting finetuned adapters has become a cornerstone in generative image models, facilitating customized image creation while minimizing storage requirements. This transition has catalyzed the development of expansive open-source platforms, fostering communities to innovate and exchange various adapters and model checkpoints, thereby propelling the proliferation of creative AI art. With over 100,000 adapters now available, the…

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Researchers at NVIDIA AI Introduce ‘VILA’: A Vision Language Model that can Reason Among Multiple Images, Learn in Context, and Even Understand Videos

The rapid evolution in AI demands models that can handle large-scale data and deliver accurate, actionable insights. Researchers in this field aim to create systems capable of continuous learning and adaptation, ensuring they remain relevant in dynamic environments. A significant challenge in developing AI models lies in overcoming the issue of catastrophic forgetting, where models…

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