Computer Vision

Incorporating demonstrating examples, known as in-context learning (ICL), significantly enhances large language models (LLMs) and large multimodal models (LMMs) without requiring parameter updates. Recent studies confirm the efficacy of few-shot multimodal ICL, particularly in improving LMM...
Machine learning models, which can contain billions of parameters, require sophisticated methods to fine-tune their performance efficiently. Researchers aim to enhance the accuracy of these models while minimizing the computational resources needed. This improvement is crucial...

Vision Transformers (ViTs) vs Convolutional Neural Networks (CNNs) in AI Image Processing

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies....

THRONE: Advancing the Evaluation of Hallucinations in Vision-Language Models

Understanding and mitigating hallucinations in vision-language models (VLVMs) is an emerging field of research that addresses the generation of coherent but factually incorrect responses...

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...

Microsoft AI Proposes an Automated Pipeline that Utilizes GPT-4V(ision) to Generate Accurate Audio Description AD for Videos

The introduction of Audio Description (AD) marks a big step towards making video content more accessible. AD provides a spoken narrative of important visual...

An Overview of Three Prominent Systems for Graph Neural Network-based Motion Planning

Graph Neural Network (GNN)--based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. This...

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...

This AI Paper from China Introduces TinyChart: An Efficient Multimodal Large Language Models MLLMs for Chart Understanding with Only 3B Parameters

Charts have become indispensable tools for visualizing data in information dissemination, business decision-making, and academic research. As the volume of multimodal data grows, a...

InternVL 1.5 Advances Multimodal AI with High-Resolution and Bilingual Capabilities in Open-Source Models

Multimodal large language models (MLLMs) integrate text and visual data processing to enhance how artificial intelligence understands and interacts with the world. This area...

SEED-Bench-2-Plus: An Extensive Benchmark Specifically Designed for Evaluating Multimodal Large Language Models (MLLMs) in Text-Rich Scenarios

Evaluating Multimodal Large Language Models (MLLMs) in text-rich scenarios is crucial, given their increasing versatility. However, current benchmarks mainly assess general visual comprehension, overlooking...

This AI Paper from Apple Introduces a Weakly-Supervised Pre-Training Method for Vision Models Using Publicly Available Web-Scale Image-Text Data

In recent times, contrastive learning has become a potent strategy for training models to learn efficient visual representations by aligning image and text embeddings....

Mixture of Data Experts (MoDE) Transforms Vision-Language Models: Enhancing Accuracy and Efficiency through Specialized Data Experts in Noisy Environments

The interdisciplinary domain of vision-language representation seeks innovative methods to develop systems to understand the nuanced interactions between text and images. This area is...

SEED-X: A Unified and Versatile Foundation Model that can Model Multi-Granularity Visual Semantics for Comprehension and Generation Tasks

In artificial intelligence, a significant focus has been on developing models that simultaneously process and interpret multiple forms of data. These multimodal models are...

Snowflake AI Research Team Unveils Arctic: An Open-Source Enterprise-Grade Large Language Model (LLM) with...

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Snowflake AI Research has launched the Arctic, a cutting-edge open-source large language model (LLM) specifically designed for enterprise AI applications, setting a new standard...

Google DeepMind Releases RecurrentGemma: One of the Strongest 2B-Parameter Open Language Models Designed for...

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Language models are the backbone of modern artificial intelligence systems, enabling machines to understand and generate human-like text. These models, which process and predict...

Finally, the Wait is Over: Meta Unveils Llama 3, Pioneering a New Era in...

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Meta has revealed its latest large language model, the Meta Llama 3, which is a major breakthrough in the field of AI. This new model is not just...

TrueFoundry Releases Cognita: An Open-Source RAG Framework for Building Modular and Production-Ready Applications

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The field of artificial intelligence is rapidly evolving, andย takingย a prototype to production stage can be quite challenging. However, TrueFoundry has recently introduced a new...

Meet Zamba-7B: Zyphra’s Novel AI Model That’s Small in Size and Big on Performance

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In the race to create more efficient and powerful AI models, Zyphra has unveiled a significant breakthrough with its new Zamba-7B model. This compact,...

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