Generative models have revolutionized fields like language, vision, and biology through their ability to learn and sample from complex data […]
Category: Computer Vision
Researchers from MIT, Google DeepMind, and Oxford Unveil Why Vision-Language Models Do Not Understand Negation and Proposes a Groundbreaking Solution
Vision-language models (VLMs) play a crucial role in multimodal tasks like image retrieval, captioning, and medical diagnostics by aligning visual […]
Researchers from China Develop Advanced Compression and Learning Techniques to process Long-Context Videos at 100 Times Less Compute
One of the most significant and advanced capabilities of a multimodal large language model is long-context video modeling, which allows […]
GameFactory: Leveraging Pre-trained Video Models for Creating New Game
Video diffusion models have emerged as powerful tools for video generation and physics simulation, showing promise in developing game engines. […]
Meet OmAgent: A New Python Library for Building Multimodal Language Agents
Understanding long videos, such as 24-hour CCTV footage or full-length films, is a major challenge in video processing. Large Language […]
Purdue University Researchers Introduce ETA: A Two-Phase AI Framework for Enhancing Safety in Vision-Language Models During Inference
Vision-language models (VLMs) represent an advanced field within artificial intelligence, integrating computer vision and natural language processing to handle multimodal […]
Researchers from Meta AI and UT Austin Explored Scaling in Auto-Encoders and Introduced ViTok: A ViT-Style Auto-Encoder to Perform Exploration
Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements […]
ByteDance Researchers Introduce Tarsier2: A Large Vision-Language Model (LVLM) with 7B Parameters, Designed to Address the Core Challenges of Video Understanding
Video understanding has long presented unique challenges for AI researchers. Unlike static images, videos involve intricate temporal dynamics and spatial-temporal […]
Revolutionizing Vision-Language Tasks with Sparse Attention Vectors: A Lightweight Approach to Discriminative Classification
Generative Large Multimodal Models (LMMs), such as LLaVA and Qwen-VL, excel in vision-language (VL) tasks like image captioning and visual […]
Meet VideoRAG: A Retrieval-Augmented Generation (RAG) Framework Leveraging Video Content for Enhanced Query Responses
Video-based technologies have become essential tools for information retrieval and understanding complex concepts. Videos combine visual, temporal, and contextual data, […]