Developing effective multi-modal AI systems for real-world applications requires handling diverse tasks such as fine-grained recognition, visual grounding, reasoning, and […]
Category: Computer Vision
Meta AI Introduces CLUE (Constitutional MLLM JUdgE): An AI Framework Designed to Address the Shortcomings of Traditional Image Safety Systems
The rapid growth of digital platforms has brought image safety into sharp focus. Harmful imagery—ranging from explicit content to depictions […]
This AI Paper Introduces Toto: Autoregressive Video Models for Unified Image and Video Pre-Training Across Diverse Tasks
Autoregressive pre-training has proved to be revolutionary in machine learning, especially concerning sequential data processing. Predictive modeling of the following […]
Sa2VA: A Unified AI Framework for Dense Grounded Video and Image Understanding through SAM-2 and LLaVA Integration
Multi-modal Large Language Models (MLLMs) have revolutionized various image and video-related tasks, including visual question answering, narrative generation, and interactive […]
ProVision: A Scalable Programmatic Approach to Vision-Centric Instruction Data for Multimodal Language Models
The rise of multimodal applications has highlighted the importance of instruction data in training MLMs to handle complex image-based queries […]
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 […]