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Introducing Meta Perception Encoder: a vision encoder setting new standards in image & video tasks. It excels in zero-shot classification & retrieval, surpassing existing models. Learn more about Meta Perception Encoder, read the research paper, and download the code and dataset

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AI at Meta

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74,392 görüntüleme • 1 yıl önce •via X (Twitter)

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अंग्रेजी साहित्य profil fotoğrafı
अंग्रेजी साहित्य1 yıl önce

Help to excelsheet❓

Rainmaker profil fotoğrafı
Rainmaker1 yıl önce

Decode the labor market! Learn how to track jobless claims using FRED and Python in my latest free Substack post. 📈 A must-read for data enthusiasts & economists. Dive into how data insights can shape your understanding of the economy.

WhaleX profil fotoğrafı
WhaleX1 yıl önce

"A vision encoder setting new standards in image & video tasks, excelling in zero-shot classification & retrieval."

Guinther Kovalski profil fotoğrafı
Guinther Kovalski1 yıl önce

just impressive how Siglip stills so close with less than 1/6 of the parameters @giffmana

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Zoom1 yıl önce

It’s over bro, rest.

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Thomas | Æ1 yıl önce

Its ability to excel in zero-shot tasks pushes the boundaries of image and video processing. Can’t wait to dive into the research and see how it outperforms current models.

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Reji Modiyil1 yıl önce

@AIatMeta, this could be a game-changer in visual technology. excited to see its impact.

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Jesse Campbell1 yıl önce

Ok...? What is it?

Jack Assery profil fotoğrafı
Jack Assery1 yıl önce

Interesting 👀

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1st Amendment1 yıl önce

42 Homies 😒

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Breck to the Future1 yıl önce

Incredible progress here. Meta Perception Encoder shows what's possible when you unify architecture across image and video tasks. Zero-shot performance is no longer optional... it's the new baseline. Excited to see how this accelerates real-world applications. Always looking to the future!

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Introducing Meta Perception Language Model (PLM): an open & reproducible vision-language model tackling challenging visual tasks. Learn more about how PLM can help the open source community build more capable computer vision systems. Read the research paper, and download the code and dataset:
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Introducing Meta Perception Language Model (PLM): an open & reproducible vision-language model tackling challenging visual tasks. Learn more about how PLM can help the open source community build more capable computer vision systems. Read the research paper, and download the code and dataset:

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🚀 Meta FAIR is releasing several new research artifacts on our road to advanced machine intelligence (AMI). These latest advancements are transforming our understanding of perception. 1️⃣ Meta Perception Encoder: A large-scale vision encoder that excels across several image & video tasks. 2️⃣ Meta Perception Language Model: A fully open & reproducible vision-language model designed to tackle visual recognition tasks. 3️⃣ Meta Locate 3D: An end-to-end model for accurate object localization in 3D environments. 4️⃣ Releasing model weights for our 8B-parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. 5️⃣Collaborative Reasoner: A framework for evaluating & improving collaborative reasoning skills in language models. Download the code, datasets, and research papers and learn more about how these artifacts are paving the way for more efficient and accurate AI systems.➡️

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Project Aria is a research program helping Meta unlock new possibilities of how we connect with and experience the world through AR and AI. 📺Watch the full tutorial from #CVPR2022 to learn how Project Aria will advance machine perception and AI research:

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Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️

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