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Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥 > Three new models: 3B, 10B, 28B with res 224, 448 💙 > Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯

merve

88,374 subscribers

83,712 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

11 Kommentare

Profilbild von merve

mervevor 1 Jahr

All models and the demo are here 🤝 Read our blog to learn more 📚

Profilbild von merve

mervevor 1 Jahr

@onuralpszr this might interest you!

Profilbild von Quantify Funds

Quantify Fundsvor 1 Jahr

Watch this space for the reveal 👀 BIG NEWS: Our family of ETFs is expanding with 10 new single stock offerings. Read the pre-effective prospectus @ #STKd #QuantifyFunds #QuantifyChaos #Nasdaq #ETFs

Profilbild von ondevice

ondevicevor 1 Jahr

love how, usually when we come across something from google, we usually see demos rather than just academic benchmarks thanks team! love this @DynamicWebPaige @OfficialLoganK @AarushSelvan

Profilbild von lambda

lambdavor 1 Jahr

openAI should take notes. Even google is open sourcing

Profilbild von merve

mervevor 1 Jahr

google has been open sourcing since early times and for a long while actually

Profilbild von nonc3ai

nonc3aivor 1 Jahr

Great news, 3B should be small enough to run on a phone, right?

Profilbild von merve

mervevor 1 Jahr

if you quantize, yes ☺️

Profilbild von Arpit Sharma

Arpit Sharmavor 1 Jahr

AI that sees, understands, and segments. Impressive!

Profilbild von Matthew Rogers

Matthew Rogersvor 1 Jahr

I want to scan my mail, lets go!

Profilbild von Furkan Gözükara

Furkan Gözükaravor 1 Jahr

Why res is still to low?

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AK

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ComfyUI

218,749 Aufrufe • vor 2 Monaten

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AI at Meta

310,281 Aufrufe • vor 1 Jahr

A new SEO tactic people are doing is they're making pages with “Reddit” in the URL slug, “Reddit” in the page title, “Reddit” in the H1… They are literally targeting the long searches that ChatGPT or other LLMs do which contain “Reddit.” Large language models will do searches that have the word “Reddit” in them. But the large language models won't search the site Reddit, they will just search Google for searches plus the word “Reddit.” So SEOs target these exact searches. Then of course, a bunch of Reddit results will come up, but their results come up as well, then they can influence the output from large language models. Crazy tactic, credit to the legendary SEO, David G Quaid (SEO), for sharing this.

A new SEO tactic people are doing is they're making pages with “Reddit” in the URL slug, “Reddit” in the page title, “Reddit” in the H1… They are literally targeting the long searches that ChatGPT or other LLMs do which contain “Reddit.” Large language models will do searches that have the word “Reddit” in them. But the large language models won't search the site Reddit, they will just search Google for searches plus the word “Reddit.” So SEOs target these exact searches. Then of course, a bunch of Reddit results will come up, but their results come up as well, then they can influence the output from large language models. Crazy tactic, credit to the legendary SEO, David G Quaid (SEO), for sharing this.

Edward Sturm

36,737 Aufrufe • vor 1 Monat

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n

Amir Zamir

73,244 Aufrufe • vor 1 Jahr

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

Qwen

215,137 Aufrufe • vor 1 Jahr

A new robot policy just cleaned up a kitchen it had never seen before [watch what happens. paper included ⬇️] Pi-0.5 builds on top of Pi-0 and shows how smart co-training with diverse data can unlock real generalization in the home. It doesn’t just learn from one setup but adapts to many, including homes it’s never seen. What it does ✅ Handles new homes without training in them ✅ Follows complex language instructions ✅ Cleans, places dishes, handles spills ✅ Matches in-home training models using cross-embodiment and web data Robots that understand tasks and adapt to new spaces are finally within reach. More in the blog: Read the paper ⬇️ Physical Intelligence, co-founded by UC Berkeley professor Sergey Levine, is a robotics startup developing general-purpose AI foundation models that enable robots to perform a wide variety of real-world tasks with human-like adaptability, recently raising $400 million to advance this vision.

A new robot policy just cleaned up a kitchen it had never seen before [watch what happens. paper included ⬇️] Pi-0.5 builds on top of Pi-0 and shows how smart co-training with diverse data can unlock real generalization in the home. It doesn’t just learn from one setup but adapts to many, including homes it’s never seen. What it does ✅ Handles new homes without training in them ✅ Follows complex language instructions ✅ Cleans, places dishes, handles spills ✅ Matches in-home training models using cross-embodiment and web data Robots that understand tasks and adapt to new spaces are finally within reach. More in the blog: Read the paper ⬇️ Physical Intelligence, co-founded by UC Berkeley professor Sergey Levine, is a robotics startup developing general-purpose AI foundation models that enable robots to perform a wide variety of real-world tasks with human-like adaptability, recently raising $400 million to advance this vision.

Ilir Aliu - eu/acc

18,120 Aufrufe • vor 1 Jahr