The rise of multimodal applications has highlighted the importance of instruction data in training MLMs to handle complex image-based queries […]
Category: Computer Vision
Content-Adaptive Tokenizer (CAT): An Image Tokenizer that Adapts Token Count based on Image Complexity, Offering Flexible 8x, 16x, or 32x Compression
One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content […]
This AI Paper Introduces Virgo: A Multimodal Large Language Model for Enhanced Slow-Thinking Reasoning
Artificial intelligence research has steadily advanced toward creating systems capable of complex reasoning. Multimodal large language models (MLLMs) represent a […]
HBI V2: A Flexible AI Framework that Elevates Video-Language Learning with a Multivariate Co-Operative Game
Video-Language Representation Learning is a crucial subfield of multi-modal representation learning that focuses on the relationship between videos and their […]
EPFL Researchers Releases 4M: An Open-Source Training Framework to Advance Multimodal AI
Multimodal foundation models are becoming increasingly relevant in artificial intelligence, enabling systems to process and integrate multiple forms of data—such […]
VITA-1.5: A Multimodal Large Language Model that Integrates Vision, Language, and Speech Through a Carefully Designed Three-Stage Training Methodology
The development of multimodal large language models (MLLMs) has brought new opportunities in artificial intelligence. However, significant challenges persist in […]
From Latent Spaces to State-of-the-Art: The Journey of LightningDiT
Latent diffusion models are advanced techniques for generating high-resolution images by compressing visual data into a latent space using visual […]
DiTCtrl: A Training-Free Multi-Prompt Video Generation Method Under MM-DiT Architectures
Generative AI has revolutionized video synthesis, producing high-quality content with minimal human intervention. Multimodal frameworks combine the strengths of generative […]
ByteDance Research Introduces 1.58-bit FLUX: A New AI Approach that Gets 99.5% of the Transformer Parameters Quantized to 1.58 bits
Vision Transformers (ViTs) have become a cornerstone in computer vision, offering strong performance and adaptability. However, their large size and […]
Collective Monte Carlo Tree Search (CoMCTS): A New Learning-to-Reason Method for Multimodal Large Language Models
In today’s world, Multimodal large language models (MLLMs) are advanced systems that process and understand multiple input forms, such as […]