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The VAR model, a Visual AutoRegressive framework that introduces a novel multi‑scale “next‑scale prediction” paradigm, dramatically improves image generation efficiency and quality, surpasses diffusion models, validates scaling laws in vision, and earned the Best Paper award at NeurIPS 2024.
The article reviews why visual ability is essential for artificial general intelligence, compares native multimodal and expert‑stitching integration approaches, details the architectures of models such as KOSMOS‑1, PALM‑E, Flamingo, BLIP‑2, LLAVA, miniGPT‑4, and introduces the SEEChat project that fuses CLIP vision encoders with chatGLM6B via a projection layer, presenting its training pipeline, experimental results, and future directions.
This presentation outlines the fundamentals of computer vision, discusses key factors such as algorithm research, large‑scale training platforms, intelligent data processing, and hardware optimization, and shares practical experiences from DeepGlint on building a world‑class visual AI system and its real‑world applications.