CLIP или SigLIP. База по Computer vision собеседованиям. Middle/Senior

Вопросы о CLIP-моделях встречаются почти на каждом техническом собеседовании. Неважно, занимаетесь ли вы видеоаналитикой, создаёте генеративные модели или работаете над поиском по изображениям — CLIP и его потомки ( BLIP , SigLIP ) стали стандартом де-факто в задачах связи визуальных и текстовых данных. Почему? Потому что они позволяют решать задачи, которые ранее требовали значительных усилий

https://habr.com/ru/articles/908168/

#clip #SigLIP #компьютерное_зрение #computervision #ml #машинное+обучение #собеседование_вопросы #собеседование_в_it #comfyui

⚡ Leverages #StableDiffusion and #SigLIP for high-fidelity visual conditioning
📊 Outperforms existing methods across multiple metrics (SSIM, FID, DISTS)
🔬 Research demonstrates superior detail preservation in patterns, logos, and textures

Edge-Ready #Vision Language Model Advances Visual #AI Processing 🌟

🧠 #OmniVision (968M params) sets new benchmark as world's smallest #VisionLanguageModel

🔄 Architecture combines #Qwen2 (0.5B) for text & #SigLIP (400M) for vision processing

💡 Key Innovations:
• 9x token reduction (729 → 81) for faster processing
• Enhanced accuracy through #DPO training
• Only 988MB RAM & 948MB storage required
• Outperforms #nanoLLAVA across multiple benchmarks

🎯 Use Cases:
• Image analysis & description
• Visual memory assistance
• Recipe generation from food images
• Technical documentation support

Try it now: https://huggingface.co/spaces/NexaAIDev/omnivlm-dpo-demo
Source: https://nexa.ai/blogs/omni-vision

🔍 Major breakthrough in multimodal AI research:

#InfinityMM dataset launches with 43.4M entries across 4 categories: 10M image descriptions, 24.4M visual instructions, 6M high-quality instructions & 3M #AI generated data

🧠 Technical highlights:

New #AquilaVL2B model uses #LLaVA architecture with #Qwen25 language model & #SigLIP for image processing
Despite only 2B parameters, achieves state-of-the-art results in multiple benchmarks
Exceptional performance: #MMStar (54.9%), #MathVista (59%), #MMBench (75.2%)

🚀 Training innovation:

4-stage training process with increasing complexity
Combines image recognition, instruction classification & response generation
Uses #opensource models like RAM++ for data generation

💡 Industry impact:

Model trained on both #Nvidia A100 GPUs & Chinese chips
Complete dataset & model available to research community
Shows promising results compared to commercial systems like #GPT4V

https://arxiv.org/abs/2410.18558