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NVIDIA drop a 3B vision-language model for fast, high-quality visual grounding in real time accurately. - Parallel box decoding - 10× faster than Qwen3-VL - Trained on 138M queries/785M boxes - GUI, OCR, and document layout, dense detection - Open source Useful for computer-use agents and Physical AI,

Md Ismail Šojal 🕷️

57,104 subscribers

131,447 次观看 • 4 天前 •via X (Twitter)

新闻政治教育科学技术

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14 条评论

Md Ismail Šojal 🕷️ 的头像

Md Ismail Šojal 🕷️4 天前

Worth a look if you work on agents, robotics, or document AI. -

falky 的头像

falky4 天前

shit is 4 months old, fucking clickbait

Fajar M Reza 的头像

Fajar M Reza4 天前

Does the 10× speedup hold on GUI workloads, or mainly dense grounding?

Samurai.AI 的头像

Samurai.AI4 天前

@skalskip92

SHK 的头像

SHK4 天前

@grok find this model to download

Dylan Colquhoun 的头像

Dylan Colquhoun4 天前

Whats the optimal packing of 17 boxes in a square?

Derek Chia 的头像

Derek Chia4 天前

Parallel box decoding sounds like the real trick. How does it hold up on dense GUI screens versus Qwen3-VL?

CEO del Socialismo de Mercado🌹🕊️ 市场社会主义CEO 的头像

CEO del Socialismo de Mercado🌹🕊️ 市场社会主义CEO4 天前

Locate Anything?

Pies Avalon 的头像

Pies Avalon4 天前

The final form of this will be an api service like jev.

Jim Wallace 的头像

Jim Wallace4 天前

That scene in The Matrix Reloaded could use some DLSS5 treatment tho

Artzy 的头像

Artzy4 天前

The throughput on that box decoding is wild

安叫兽|Bird🕊️ 🔶 BNB 的头像

安叫兽|Bird🕊️ 🔶 BNB4 天前

3B 还能跑这么快，GUI 场景应该挺香，等实测了

Antonio Rocha 的头像

Antonio Rocha4 天前

É mais rapido que o YOLO 26?

Ramesh Devasi 的头像

Ramesh Devasi4 天前

why i am not able to get simple 4 corner flat planer tracker which can run in web

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NVIDIA's LocateAnything is a new vision model for grounding and detection. Very performant and accurate! > 10x faster than Qwen3-VL > 138M queries + 785M boxes > GUI, OCR, docs, dense detection > Free & open source

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12,613 次观看 • 1 个月前

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59,959 次观看 • 1 年前

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18,196 次观看 • 11 个月前

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120,072 次观看 • 1 年前

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90,144 次观看 • 9 个月前

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80,597 次观看 • 1 年前

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48,170 次观看 • 1 年前

People are overlooking Google Gemini Realtime models for computer use It gave me sub 100ms latency with computer use. It has a much larger context window and is much cheaper as well Combine that with local OCR and local screen detection model based on Omniparser by Microsoft it works under 100ms action taking when combined with Cua I also put in a harness for Nous Research Hermes with it. You can access it all at your tip of your cursor. You can draw on your screen to give your agents a context And I am making it Open Source! Link in the Comments Sundar Pichai Min-Liang Tan Mojtaba Seyedhosseini

People are overlooking Google Gemini Realtime models for computer use It gave me sub 100ms latency with computer use. It has a much larger context window and is much cheaper as well Combine that with local OCR and local screen detection model based on Omniparser by Microsoft it works under 100ms action taking when combined with Cua I also put in a harness for Nous Research Hermes with it. You can access it all at your tip of your cursor. You can draw on your screen to give your agents a context And I am making it Open Source! Link in the Comments Sundar Pichai Min-Liang Tan Mojtaba Seyedhosseini

Milind S

38,361 次观看 • 4 个月前

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

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kwindla

42,246 次观看 • 1 年前

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70,895 次观看 • 5 个月前