How AI Vision Evolved | Merve Noyan

2026-04-06 · Source: HuggingFace · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, short

Summary

The field of computer vision has rapidly evolved, transitioning from convolutional neural networks (CNNs) to Vision Transformers (ViTs), which significantly improved scalability and enabled extensive transfer learning for tasks like object detection, image segmentation, and image classification. This progression led to a "GPT moment" in vision with the introduction of LLaVA, an open-source architecture capable of processing image and text prompts to generate text outputs, akin to a large language model (LLM) with image input capabilities. LLaVA's design, which involves connecting an image encoder (e.g., CLIP) to an LLM via a projection layer, made it highly scalable and easier to train compared to earlier closed models like Flamingo or open implementations like IDEFICS. The current landscape sees a saturation in architectural improvements, with focus shifting towards continuous training of existing models like Alibaba's Qwen, developing advanced alignment techniques such as reinforcement learning from human feedback for multimodal systems, and improving benchmarks in document understanding, video understanding, and agentic vision reasoning, exemplified by models like Kimi that can generate website code from images.

Key takeaway

For AI Engineers and Research Scientists developing multimodal systems, the shift towards Vision-Language Models (VLMs) like LLaVA signifies a critical architectural pivot. You should prioritize integrating scalable, open-source VLM frameworks and focus on advanced alignment techniques rather than solely pursuing novel "vanilla vision" architectures. Consider adapting existing VLM architectures for tasks like video understanding or interleaved image-text processing to drive innovation.

Key insights

Vision models have evolved from CNNs to ViTs and now to scalable, open-source Vision-Language Models like LLaVA.

Principles

• Vision Transformers enhance scalability and transfer learning.
• Projection layers can effectively bridge image encoders and LLMs.
• Multimodal alignment techniques are crucial for advanced VLMs.

Method

LLaVA's training involves connecting an image encoder (e.g., CLIP) to an LLM via a projection layer, followed by instruction fine-tuning with image-text pairs, enabling multimodal understanding.

In practice

• Utilize ViTs for robust image classification and segmentation.
• Explore LLaVA for multimodal text generation from images.
• Investigate Kimi for image-to-website code generation.

Topics

• Vision Transformers
• LLaVA
• Vision Language Models
• Multimodal AI
• Reinforcement Learning from Human Feedback

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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• Merve Noyan: The Future of Vision in ML - HF Podcast #1 — Vision models are maturing, shifting from architectural breakthroughs to optimization and integration with language for real-world applications.
• Vision models — LLaVA 1.6 models offer enhanced vision capabilities, higher resolution, and improved text recognition under permissive licenses.
• The Hidden Evolution of Disguised Visual Context inside the VLM — Visual token integration paradigms in VLMs dictate internal representation evolution, affecting feature utilization and task performance.
• Data Machina #260 — Small, specialized Vision-Language Models are achieving powerful performance with greater efficiency and openness.
• From Pixels to Prompts: Vision-Language Models — Understanding Vision-Language Models requires a clear mental map beyond just knowing buzzwords.
• You’ve Been Thinking About Multimodal LLMs Wrong — Here’s the Architecture That Changes Everything — Early fusion combines text and visual tokens at the input, enabling unified, simultaneous multimodal processing.

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