AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#AI #chatGPT
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💥 GaussianGPT 3D GSC💥

👉From TUM, GaussianGPT: transformer-based 3D Gaussians generation via next-token prediction -> full 3D complex indoor scene. Repo announced💙

👉Review https://t.ly/bj-lL
👉Paper arxiv.org/pdf/2603.26661
👉Project nicolasvonluetzow.github.io/GaussianGPT/
👉Repo TBA
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👌HandX: Scaling Hands Motion👌

👉 HandX is a unified foundation spanning data, annotation, and evaluation: novel large-scale dataset of bimanual & dexterous motions with fine-grained textual. Around 6M frames. Repo available💙

👉Review https://t.ly/1nGxw
👉Paper https://arxiv.org/pdf/2603.28766
👉Project https://handx-project.github.io/
👉Repo github.com/handx-project/HandX
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🌵SOTA Training-Free In-Context Segmentation🌵

👉INSID3 is the new SOTA, training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. Repo under Apache 2.0💙

👉Review https://t.ly/NVWHN
👉Paper arxiv.org/pdf/2603.28480
👉Project visinf.github.io/INSID3/
👉Repo github.com/visinf/INSID3
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🪬Camera Raw Image Generation🪬

👉RawGen by #Samsung is a generative approach that learns the complex distribution of raw sensor data directly, enabling high-fidelity generation from either text descriptions or standard sRGB images across arbitrary camera sensors. Linear raw image once, then apply any ISP operation. Repo announced💙

👉Review https://t.ly/_QVKP
👉Paper https://arxiv.org/pdf/2604.00093
👉Project https://dy112.github.io/rawgen-page/
👉Repo TBA
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If you have to invest TODAY 1B$ on a frontier tech for the next decade, would you invest in space, agentic, quantum or frugal GPUs? Vote here: https://t.ly/hSx6i
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🍎Video Object Deletion🍎

👉Void by Netflix is a novel video object removal framework designed to perform physically-plausible inpainting in very complex scenarios. Repo under Apache 2.0💙

👉Review https://t.ly/cMVny
👉Paper https://arxiv.org/pdf/2604.02296
👉Project https://void-model.github.io/
👉Repo https://github.com/Netflix/void-model
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