FERRET-UI 2: MASTERING UNIVERSAL USER INTERFACE UNDERSTANDING ACROSS PLATFORMS | #ai #2024 #genai
AI Today27 Nov 2024

FERRET-UI 2: MASTERING UNIVERSAL USER INTERFACE UNDERSTANDING ACROSS PLATFORMS | #ai #2024 #genai

Paper: https://arxiv.org/pdf/2410.18967 The paper introduces Ferret-UI 2, a multimodal large language model (MLLM) that significantly improves upon its predecessor, Ferret-UI, by enabling universal user interface (UI) understanding across diverse platforms (iPhone, Android, iPad, webpages, and AppleTV). Key improvements include multi-platform support, high-resolution perception through adaptive scaling, and advanced task training data generation using GPT-4o with visual prompting. Ferret-UI 2 demonstrates superior performance on various benchmarks, showcasing strong cross-platform transfer capabilities and surpassing existing models in UI referring, grounding, and user-centric advanced tasks. The enhanced model architecture and higher-quality training data contribute to these advancements. The authors conclude by outlining future work focusing on broader platform coverage and the development of a truly generalist UI navigation agent. ai , artificial intelligence , arxiv , research , paper , publication , llm, genai, generative ai , large visual models, large language models, large multi modal models, nlp, text, machine learning, ml, nividia, openai, anthropic, microsoft, google, technology, cutting-edge, meta, llama, chatgpt, gpt, elon musk, sam altman, deployment, engineering, scholar, science, apple, samsung

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SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with MotionAware Mem | #2024

SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with MotionAware Mem | #2024

Paper: https://arxiv.org/pdf/2411.11922 Github: https://github.com/yangchris11/samurai Blog: https://yangchris11.github.io/samurai/ The paper introduces SAMURAI, a novel visual object tracking method...

27 Nov 202414min

Adding Error Bars to Evals: A Statistical Approach to LM Evaluations | #llm #genai #anthropic #2024

Adding Error Bars to Evals: A Statistical Approach to LM Evaluations | #llm #genai #anthropic #2024

Github: https://arxiv.org/pdf/2411.00640 This research paper advocates for incorporating rigorous statistical methods into the evaluation of large language models (LLMs). It introduces formulas for c...

27 Nov 202414min

Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions | #ai #llm #alibaba #genai #2024

Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions | #ai #llm #alibaba #genai #2024

Paper: https://arxiv.org/pdf/2411.14405 Github: https://github.com/AIDC-AI/Marco-o1 The Alibaba MarcoPolo team introduces Marco-o1, a large reasoning model designed to excel in open-ended problem-sol...

27 Nov 202414min

FLUX.I TOOLS | #ai #computervision #cv #BlackForestLabs #2024

FLUX.I TOOLS | #ai #computervision #cv #BlackForestLabs #2024

Github: https://github.com/black-forest-labs/... Black Forest Labs announced FLUX.1 Tools, a suite of four open-access and API-based models enhancing their FLUX.1 text-to-image model. FLUX.1 Fill exc...

27 Nov 202414min

Tülu 3 opens language model post-training up to more tasks and more people | #ai #llm #allenai #2024

Tülu 3 opens language model post-training up to more tasks and more people | #ai #llm #allenai #2024

Blog: https://allenai.org/blog/tulu-3 Summary The Allen Institute for Artificial Intelligence (Ai2) has released Tülu 3, an open-source family of post-trained language models. Unlike closed models fr...

27 Nov 202414min

Multimodal Autoregressive Pre-training of Large Vision Encoders | #ai #computervision #apple #2024

Multimodal Autoregressive Pre-training of Large Vision Encoders | #ai #computervision #apple #2024

Paper: https://arxiv.org/pdf/2411.14402 Github Link: https://github.com/apple/ml-aim This research introduces AIMV2, a family of large-scale vision encoders pre-trained using a novel multimodal auto...

27 Nov 202414min

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