Data Machina #260

2019-03-12 · Source: Data Machina · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Advanced, short

Summary

Vision-Language Models (VLMs) are rapidly advancing, with large foundation models like OpenAI GPT-4o and Google Gemini Pro 1.5 dominating benchmarks. Concurrently, a significant trend is emerging towards smaller, more efficient, and specialized VLMs that offer powerful capabilities at lower operational costs. Notable examples include LLaVA-Next, which uses an image-text interleaved format for multi-image, video, and 3D tasks, and PaliGemma, an open, lightweight VLM for detailed image-text Q&A. Microsoft's Phi-3 Vision provides high-quality multimodal reasoning, while Florence-2 unifies various vision and vision-language tasks via prompt-based representations. InternLM-XComposer 2.5 stands out for its ultra-high resolution and long-context understanding, achieving GPT-4V level performance with a 7B LLM backend. NVIDIA has also released a VLM playground for exploration, and LMSYS.org introduced a new multi-modal benchmark.

Key takeaway

For AI Architects and AI Engineers evaluating VLM solutions, consider the emerging class of small, powerful, and often open-source VLMs. These models, such as LLaVA-Next or InternLM-XComposer 2.5, offer competitive performance and specialized features at a potentially lower operational cost than larger proprietary models, enabling more efficient deployment and fine-tuning for specific applications.

Key insights

Small, specialized Vision-Language Models are achieving powerful performance with greater efficiency and openness.

Principles

• Interleaved image-text formats unify diverse multimodal tasks.
• Prompt-based representations streamline vision and VLM tasks.

Method

Several small VLMs utilize specific architectural choices like interleaved image-text formats or prompt-based representations to achieve high performance across diverse vision and language tasks, often with long context capabilities.

In practice

• Explore LLaVA-Next for unified multi-image/video processing.
• Use PaliGemma for efficient image-text question answering.
• Test Phi-3 Vision for high-quality multimodal reasoning.

Topics

• Vision-Language Models
• Multimodal AI
• Open-source VLMs
• AI Model Benchmarking
• Foundation Models

Code references

• LLaVA-VL/LLaVA-NeXT
• InternLM/InternLM-XComposer
• microsoft/graphrag
• mindsdb/mindsdb
• yandex-research/tabred

Best for: Computer Vision Engineer, AI Architect, AI Engineer, AI Researcher, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Machina.