Загрузка видео...

Не удалось загрузить видео

На главную

👀 Seeing is just the beginning. With Qwen-MM-Plugins, turn your favorite agent harness multimodal-native — read images, videos & documents, edit videos, work with 3D/CAD, and more. From multimodal models → multimodal agents. 🚀 Watch it in action:

Alibaba_Qwen's profile picture

Qwen

263,667 subscribers

728,257 просмотров • 1 день назад •via X (Twitter)

Наука и технологииОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Introducing GLM-5V-Turbo: Vision Coding Model - Native Multimodal Coding: Natively understands multimodal inputs including images, videos, design drafts, and document layouts. - Balanced Visual and Programming Capabilities: Achieves leading performance across core benchmarks for multimodal coding, tool use, and GUI Agents. - Deep Adaptation for Claude Code and Claw Scenarios: Works in deep synergy with Agents like Claude Code and OpenClaw. Try it now: API: Coding Plan trial applications:
1:17

Introducing GLM-5V-Turbo: Vision Coding Model - Native Multimodal Coding: Natively understands multimodal inputs including images, videos, design drafts, and document layouts. - Balanced Visual and Programming Capabilities: Achieves leading performance across core benchmarks for multimodal coding, tool use, and GUI Agents. - Deep Adaptation for Claude Code and Claw Scenarios: Works in deep synergy with Agents like Claude Code and OpenClaw. Try it now: API: Coding Plan trial applications:

Z.ai

1,965,931 просмотров • 4 месяцев назад

This is insane. Anyone can build an AI app with no code in 5 minutes using Imagica. Plus, it's multimodal: feed it text, images, video, and 3D models 🤯
0:51

This is insane. Anyone can build an AI app with no code in 5 minutes using Imagica. Plus, it's multimodal: feed it text, images, video, and 3D models 🤯

Rowan Cheung

505,946 просмотров • 3 лет назад

Multimodal AI is here 🤯 GPT-4 can now turn your images into a text file in a snap with the new code interpreter model. Witness the OCR magic in action 🔥
0:29

Multimodal AI is here 🤯 GPT-4 can now turn your images into a text file in a snap with the new code interpreter model. Witness the OCR magic in action 🔥

Shubham Saboo

727,673 просмотров • 3 лет назад

Seedance 2.5 is now live on Byteplus Lumina! • Generate native 30s videos in a single generation • Reshape any part of the frame with next-level video editing control • Take full control of your production with up to 50 multimodal references, now with support for 3D white models
1:09

Seedance 2.5 is now live on Byteplus Lumina! • Generate native 30s videos in a single generation • Reshape any part of the frame with next-level video editing control • Take full control of your production with up to 50 multimodal references, now with support for 3D white models

Lumina

895,379 просмотров • 6 дней назад

Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️
1:19

Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️

Xiang Yue

57,699 просмотров • 1 год назад

can AI write engaging news that people can trust? introducing ✨Data2Story: a data journalist agent. give it raw data, it generate a verifiable, multimodal article. 🔍verifiable: every claim is evidence-grounded, traces back to data, code, or a cited source. 🔮multimodal: the article is a generative UI — images, videos, audio, interactive charts. not just readable, but trustworthy and playable. 🧵1/N
0:48

can AI write engaging news that people can trust? introducing ✨Data2Story: a data journalist agent. give it raw data, it generate a verifiable, multimodal article. 🔍verifiable: every claim is evidence-grounded, traces back to data, code, or a cited source. 🔮multimodal: the article is a generative UI — images, videos, audio, interactive charts. not just readable, but trustworthy and playable. 🧵1/N

Kevin Lin

32,968 просмотров • 1 месяц назад

Gemini 3.1 Flash-Lite is the model to build always on multimodal AI Agents. I just built a memory agent with Google ADK that runs 24/7. Feed it text, images, audio, video or PDFs. It reads, connects and consolidates everything while you sleep. 100% Opensource code.
1:05

Gemini 3.1 Flash-Lite is the model to build always on multimodal AI Agents. I just built a memory agent with Google ADK that runs 24/7. Feed it text, images, audio, video or PDFs. It reads, connects and consolidates everything while you sleep. 100% Opensource code.

Shubham Saboo

24,930 просмотров • 5 месяцев назад

Introducing Large Visual Memory Model (LVMM) 2.0, in partnership with Qualcomm Advanced visual AI is now on your phone, and in your home cameras. AI that understands your photos and videos, runs 100% privately and securely. ALL on-device. The beginning of on-device multimodal AI.
6:46

Introducing Large Visual Memory Model (LVMM) 2.0, in partnership with Qualcomm Advanced visual AI is now on your phone, and in your home cameras. AI that understands your photos and videos, runs 100% privately and securely. ALL on-device. The beginning of on-device multimodal AI.

Shawn Shen

1,204,602 просмотров • 9 месяцев назад

MiniMax is building with next-gen AGI, offering foundation AI models and revolutionizing content creation with AI-powered video, music, chat products. With Alibaba's one-stop multimodal data platform, MiniMax can save the complexity of handling multimodal data work with the AI-ready data foundation, and boost streamlined innovation on it. Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, shares how the right tech stack can elevate AI from potential to powerful. 🚀
1:15

MiniMax is building with next-gen AGI, offering foundation AI models and revolutionizing content creation with AI-powered video, music, chat products. With Alibaba's one-stop multimodal data platform, MiniMax can save the complexity of handling multimodal data work with the AI-ready data foundation, and boost streamlined innovation on it. Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, shares how the right tech stack can elevate AI from potential to powerful. 🚀

Alibaba Group

305,831 просмотров • 8 месяцев назад

We are excited to announce the 1st version of our multimodal assistant, Yasa-1, a language assistant with visual and auditory sensors that can take actions via code execution 🪄. Yasa-1 can understand text, images, videos, sounds & more! 🚀 Check out more details below👇
0:45

We are excited to announce the 1st version of our multimodal assistant, Yasa-1, a language assistant with visual and auditory sensors that can take actions via code execution 🪄. Yasa-1 can understand text, images, videos, sounds & more! 🚀 Check out more details below👇

Reka

814,303 просмотров • 2 лет назад

1.🚀 Baidu’s ERNIE 4.5 VL-28B-A3B-Thinking — an open-source MoE beast. With just 3B active params, it delivers SOTA multimodal reasoning: visual logic (MathVista 72.6), grounding, tool-calling, and more. The full family spans 12 models (0.3B → 424B). 🔗 Try it here: 💬 What’s your favorite “Thinking Mode” trick? #ERNIE45 #MultimodalAI
0:22

1.🚀 Baidu’s ERNIE 4.5 VL-28B-A3B-Thinking — an open-source MoE beast. With just 3B active params, it delivers SOTA multimodal reasoning: visual logic (MathVista 72.6), grounding, tool-calling, and more. The full family spans 12 models (0.3B → 424B). 🔗 Try it here: 💬 What’s your favorite “Thinking Mode” trick? #ERNIE45 #MultimodalAI

Helena AI

53,547 просмотров • 7 месяцев назад

The next generation of mobile apps will run multimodal AI locally by default. Today, we’re making it practical for developers to ship. We just launched NexaSDK for Mobile on Product Hunt. Developers can run the latest multimodal AI models fully on-device in iOS & Android apps with Apple Neural Engine and Qualcomm Hexagon NPU acceleration. In just 3 lines of code, build chat, multimodal, search, and audio features with no cloud cost, complete privacy, 2x faster speed and 9× better energy efficiency. Try out NexaSDK for Mobile and we’d love your feedback (Product Hunt link in thread).
1:21

The next generation of mobile apps will run multimodal AI locally by default. Today, we’re making it practical for developers to ship. We just launched NexaSDK for Mobile on Product Hunt. Developers can run the latest multimodal AI models fully on-device in iOS & Android apps with Apple Neural Engine and Qualcomm Hexagon NPU acceleration. In just 3 lines of code, build chat, multimodal, search, and audio features with no cloud cost, complete privacy, 2x faster speed and 9× better energy efficiency. Try out NexaSDK for Mobile and we’d love your feedback (Product Hunt link in thread).

NEXA AI

319,337 просмотров • 7 месяцев назад

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:
2:49

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Andrew Ng

74,060 просмотров • 1 год назад

Gemini Omni Flash Is Here — 50% off on Pollo AI! Create cinematic videos smarter: 1. Native multimodal input gives you cohesive, consistent, and editable outputs. 2. World knowledge baked in – from physics to history – so your scenes don't just look real, they feel real. Try now:
0:25

Gemini Omni Flash Is Here — 50% off on Pollo AI! Create cinematic videos smarter: 1. Native multimodal input gives you cohesive, consistent, and editable outputs. 2. World knowledge baked in – from physics to history – so your scenes don't just look real, they feel real. Try now:

Pollo AI

421,866 просмотров • 1 месяц назад

LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo 📖 Read the docs: 🚀 Get started with LlamaCloud:
1:02

LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo 📖 Read the docs: 🚀 Get started with LlamaCloud:

LlamaIndex 🦙

51,845 просмотров • 4 месяцев назад

We've just unveiled ERNIE 4.5 & X1! 🚀 As a deep-thinking reasoning model with multimodal capabilities, ERNIE X1 delivers performance on par with DeepSeek R1 at only half the price. Meanwhile, ERNIE 4.5 is our latest foundation model and new-generation native multimodal model. Plus, our AI chatbot ERNIE Bot has now been made free to individual users ahead of schedule. Both models are now freely accessible to all ERNIE Bot users via its official website:
0:20

We've just unveiled ERNIE 4.5 & X1! 🚀 As a deep-thinking reasoning model with multimodal capabilities, ERNIE X1 delivers performance on par with DeepSeek R1 at only half the price. Meanwhile, ERNIE 4.5 is our latest foundation model and new-generation native multimodal model. Plus, our AI chatbot ERNIE Bot has now been made free to individual users ahead of schedule. Both models are now freely accessible to all ERNIE Bot users via its official website:

Baidu Inc.

1,712,153 просмотров • 1 год назад

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.
2:21

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.

AK

23,958 просмотров • 2 лет назад

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.
0:22

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.

AK

141,440 просмотров • 2 лет назад