Image Understanding Collection

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🔮Psychic

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“OpenAI and Anthropic compatible server for Apple Silicon. Run LLMs and vision-language models (Llama, Qwen-VL, LLaVA) with continuous batching, MCP tool calling, and multimodal support. Native MLX backend, 400+ tok/s. Works with Claude Code.”

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🔮Psychic

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“A 3B-active-parameter native unified multimodal model for image and video understanding, generation, and editing.”

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🔮Psychic

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“[CVPR 2024 Highlight🔥] Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding”

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🔮Psychic

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“UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation”

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🌸Fairy

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“🎩 An Alfred 5 Workflow for using OpenAI Chat API to interact with GPT models 🤖💬 It also allows image generation/editing/understanding 🖼️, speech-to-text conversion 🎤, and text-to-speech synthesis 🔈”

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📦Normal

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“A Unified Framework for Image-to-Graph Generation. Paper accepted @ ECCV22.”

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🔮Psychic

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“[CVPR'26 Demo] Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device”

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🔮Psychic

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“Code release for Ming-UniVision: Joint Image Understanding and Geneation with a Continuous Unified Tokenizer”

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🔮Psychic

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“[ICML26 Spotlight] UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture”

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🔮Psychic

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“WACV 2024 Papers: Discover cutting-edge research from WACV 2024, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!”

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🔮Psychic

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“Official implementation of "UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis" - A unified medical vision-language model that integrates multimodal understanding and generation capabilities.”

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🔮Psychic

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“This is the implement of the paper "DynamicVis: An Efficient and General Visual Foundation Model for Remote Sensing Image Understanding"”

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🔮Psychic

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“🍑 relsim: Relational Visual Similarity | pip install relsim 🌍 (CVPR 2026)”

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📦Normal

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“Latest Advances on (RL based) Multimodal Reasoning and Generation in Multimodal LLMs”

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💎Aqua

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“Multi-Model Visual Understanding MCP Server, GLM-4.6V, DeepSeek-OCR (free), and Qwen3-VL-Flash. Provide visual processing capabilities for AI coding models that do not support image understanding.多模型视觉理解MCP服务器，GLM-4.6V、DeepSeek-OCR（免费）和Qwen3-VL-Flash等。为不支持图片理解的 AI 编码模型提供视觉处理能力。”