FLUX.I TOOLS | #ai #computervision #cv #BlackForestLabs #2024
AI Today27 Nov 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 excels at inpainting and outpainting, exceeding existing tools. FLUX.1 Canny and FLUX.1 Depth offer structural guidance using edge and depth maps, respectively, outperforming competitors. Finally, FLUX.1 Redux enables image variation and restyling. All models are available via Hugging Face and GitHub (dev versions) and the BFL API (pro versions). ai , computer vision , cv , black forest labs , artificial intelligence , arxiv , research , paper , publication

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Mixtures of In-Context Learners | #ai #genai #llm #2024 #ml

Mixtures of In-Context Learners | #ai #genai #llm #2024 #ml

Paper: https://arxiv.org/pdf/2411.02830 This research introduces Mixtures of In-Context Learners (MOICL), a novel approach to improve in-context learning (ICL) in large language models (LLMs). MOICL ...

27 Nov 202414min

LLM2CLIP: POWERFUL LM UNLOCKS RICHER VISUAL REPRESENTATION | #ai #genai #lvm #llm #mmm #cv #ms #2024

LLM2CLIP: POWERFUL LM UNLOCKS RICHER VISUAL REPRESENTATION | #ai #genai #lvm #llm #mmm #cv #ms #2024

Paper: https://arxiv.org/pdf/2411.04997 Github: https://github.com/microsoft/LLM2CLIP The paper introduces LLM2CLIP, a method to improve the visual representation learning capabilities of CLIP by int...

27 Nov 202414min

OPENSCHOLAR: SYNTHESIZING SCIENTIFICLITERATURE WITH RETRIEVAL-AUGMENTED LMS | #ai #genai #llm #2024

OPENSCHOLAR: SYNTHESIZING SCIENTIFICLITERATURE WITH RETRIEVAL-AUGMENTED LMS | #ai #genai #llm #2024

Paper: https://arxiv.org/pdf/2411.14199 Github: https://github.com/AkariAsai/OpenScholar The research introduces OpenScholar, a retrieval-augmented large language model (LLM) designed for synthesizin...

27 Nov 202414min

Bilateral Reference for High-Resolution Dichotomous Image Segmentation | #ai #genai #llm #cv #2024

Bilateral Reference for High-Resolution Dichotomous Image Segmentation | #ai #genai #llm #cv #2024

Paper: https://arxiv.org/pdf/2401.03407 Github: https://github.com/ZhengPeng7/BiRefNet This research introduces BiRefNet, a novel deep learning framework for high-resolution dichotomous image segment...

27 Nov 202414min

LLaVA-o1: Let Vision Language Models Reason Step-by-Step | #ai #genai #lvm #llm #mmm #cv #2024

LLaVA-o1: Let Vision Language Models Reason Step-by-Step | #ai #genai #lvm #llm #mmm #cv #2024

Paper: https://arxiv.org/pdf/2411.10440 Github: https://github.com/PKU-YuanGroup/LLaV... The paper introduces LLaVA-o1, a vision-language model designed for improved multi-stage reasoning. Unlike pre...

27 Nov 202414min

Model-Based Transfer Learning for Contextual Reinforcement Learning | #ai #mit #rl #genai #ml #2024

Model-Based Transfer Learning for Contextual Reinforcement Learning | #ai #mit #rl #genai #ml #2024

Paper: https://arxiv.org/pdf/2408.04498 This research introduces Model-Based Transfer Learning (MBTL), a novel framework for improving the efficiency and robustness of deep reinforcement learning (RL...

27 Nov 202414min

Diverse and Effective Red Teaming Auto-gen Rewards & Multi-step RL | #aisafety #openai #genai #2024

Diverse and Effective Red Teaming Auto-gen Rewards & Multi-step RL | #aisafety #openai #genai #2024

Paper: https://cdn.openai.com/papers/diverse... Blog: https://openai.com/index/advancing-re... This OpenAI research paper presents novel methods for automated red teaming of large language models (LL...

27 Nov 202414min

OpenAI’s Approach to External Red Teaming for AI Models and System | #aisafety #openai #genai #2024

OpenAI’s Approach to External Red Teaming for AI Models and System | #aisafety #openai #genai #2024

Paper: https://cdn.openai.com/papers/openais... Blog: https://openai.com/index/advancing-re... This white paper details OpenAI's approach to external red teaming for AI models and systems. External r...

27 Nov 202414min

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