关键词 "Fashion collections" 的搜索结果， 共 22 条, 只显示前 480 条
Virtual try on clothes with AI
Cladwell is an app that simplifies your life by creating a capsule wardrobe and providing daily outfits.
AI platform for generating fashion model imagery from flat lays.
Recovery and collections platform with machine learning
Instantly turn sketches into realistic images.
Create stunning product photos with insMind AI Generated Fashion Momels. Choose AI models from female, male, diverse skin stones, hari colors, and body sizes. Face swap to change the home-made photos
AI-powered photo editing suite for e-commerce
AI fashion model & e-commerce assistant
MCP servers collection
This is an MCP server that interacts with a PocketBase instance. It allows you to fetch, list, create, update, and manage records and files in your PocketBase collections.
MCP Server for running Bruno Collections
Swytchcode accelerates API integrations, allowing developers to seamlessly integrate any API using Postman collections or OpenAPI specifications. With Swytchcode, developers can obtain production-read
MCP Server for running Postman Collections with Newman
The Model Context Protocol (MCP) is a standardized way to supply context to large language models (LLMs). Using the MCP Python SDK, you can build servers that expose data (resources), functionality (t
Clo MCP Plugin, a C++ application built with the Clo3D SDK. It establishes a socket server within Clo3D, allowing Large Language Models (LLMs) to interact with and control Clo3D via the Model Context
MCP server for connecting to you typesense collections and retrieve data using your favorite MCP client (Claude/ Cursor)
The Milvus MCP server enables AI applications to interact with Milvus vector databases using natural language commands. It allows AI models to perform vector searches, manage collections, and retrieve
VACE（Video Creation and Editing）是阿里巴巴通义实验室推出的一站式视频生成与编辑框架。基于整合多种视频任务（如参考视频生成、视频到视频编辑、遮罩编辑等）到一个统一模型中，实现高效的内容创作和编辑功能。VACE的核心在于Video Condition Unit（VCU），将文本、图像、视频和遮罩等多种模态输入整合为统一的条件单元，支持多种任务的灵活组合。开源的 Wan2
WebThinker是中国人民大学、北京智源人工智能研究院和华为泊松实验室等机构提出的深度研究智能体。WebThinker赋能大型推理模型（LRMs）在推理过程中自主进行网络搜索、网页导航和报告撰写。WebThinker基于深度网页探索器和自主思考、搜索、写作策略，让LRMs能动态获取信息，实时生成高质量研究报告。WebThinker基于强化学习的训练策略进一步优化工具使用效率。WebThinke
NVILA是NVIDIA推出的系列视觉语言模型，能平衡效率和准确性。模型用“先扩展后压缩”策略，有效处理高分辨率图像和长视频。NVILA在训练和微调阶段进行系统优化，减少资源消耗，在多项图像和视频基准测试中达到或超越当前领先模型的准确性，包括Qwen2VL、InternVL和Pixtral在内的多种顶尖开源模型，及GPT-4o和Gemini等专有模型。NVILA引入时间定位、机器人导航和医学成像等
视觉语言模型（VLM），基于像素空间推理增强模型对视觉信息的理解和推理能力。模型能直接在视觉输入上进行操作，如放大图像区域或选择视频帧，更细致地捕捉视觉细节。Pixel Reasoner用两阶段训练方法，基于指令调优让模型熟悉视觉操作，用好奇心驱动的强化学习激励模型探索像素空间推理。Pixel Reasoner在多个视觉推理基准测试中取得优异的成绩，显著提升视觉密集型任务的性能。 Pixel R
VRAG-RL是阿里巴巴通义大模型团队推出的视觉感知驱动的多模态RAG推理框架，专注于提升视觉语言模型（VLMs）在处理视觉丰富信息时的检索、推理和理解能力。基于定义视觉感知动作空间，让模型能从粗粒度到细粒度逐步获取信息，更有效地激活模型的推理能力。VRAG-RL引入综合奖励机制，结合检索效率和基于模型的结果奖励，优化模型的检索和生成能力。在多个基准测试中，VRAG-RL显著优于现有方法，展现在视
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