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Julien BLANCHON PRO
blanchon
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Introducing Moonshine Web: real-time speech recognition running 100% locally in your browser!
🚀 Faster and more accurate than Whisper
🔒 Privacy-focused (no data leaves your device)
⚡️ WebGPU accelerated (w/ WASM fallback)
🔥 Powered by ONNX Runtime Web and Transformers.js
Demo: webml-community/moonshine-web
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/moonshine-web
🚀 Faster and more accurate than Whisper
🔒 Privacy-focused (no data leaves your device)
⚡️ WebGPU accelerated (w/ WASM fallback)
🔥 Powered by ONNX Runtime Web and Transformers.js
Demo: webml-community/moonshine-web
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/moonshine-web
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Introducing ⇆ Marigold-DC — our training-free zero-shot approach to monocular Depth Completion with guided diffusion! If you have ever wondered how else a long denoising diffusion schedule can be useful, we have an answer for you!
Depth Completion addresses sparse, incomplete, or noisy measurements from photogrammetry or sensors like LiDAR. Sparse points aren’t just hard for humans to interpret — they also hinder downstream tasks.
Traditionally, depth completion was framed as image-guided depth interpolation. We leverage Marigold, a diffusion-based monodepth model, to reframe it as sparse-depth-guided depth generation. How the turntables! Check out the paper anyway 👇
🌎 Website: https://marigolddepthcompletion.github.io/
🤗 Demo: prs-eth/marigold-dc
📕 Paper: https://arxiv.org/abs/2412.13389
👾 Code: https://github.com/prs-eth/marigold-dc
Team ETH Zürich: Massimiliano Viola ( @mviola ), Kevin Qu ( @KevinQu7 ), Nando Metzger ( @nandometzger ), Bingxin Ke ( @Bingxin ), Alexander Becker, Konrad Schindler, and Anton Obukhov ( @toshas ). We thank
Hugging Face for their continuous support.
Depth Completion addresses sparse, incomplete, or noisy measurements from photogrammetry or sensors like LiDAR. Sparse points aren’t just hard for humans to interpret — they also hinder downstream tasks.
Traditionally, depth completion was framed as image-guided depth interpolation. We leverage Marigold, a diffusion-based monodepth model, to reframe it as sparse-depth-guided depth generation. How the turntables! Check out the paper anyway 👇
🌎 Website: https://marigolddepthcompletion.github.io/
🤗 Demo: prs-eth/marigold-dc
📕 Paper: https://arxiv.org/abs/2412.13389
👾 Code: https://github.com/prs-eth/marigold-dc
Team ETH Zürich: Massimiliano Viola ( @mviola ), Kevin Qu ( @KevinQu7 ), Nando Metzger ( @nandometzger ), Bingxin Ke ( @Bingxin ), Alexander Becker, Konrad Schindler, and Anton Obukhov ( @toshas ). We thank
Hugging Face for their continuous support.
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Implements from first-principle a discrete flow matching model for code generation- trained a small sized 2D dfm model on two variations of code for binary search. The result was amazing, code in comment:
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
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Metric and Relative Monocular Depth Estimation: An Overview. Fine-Tuning Depth Anything V2 👐 📚
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