Juan Carlos Niebles

@jcniebles

Multimodal AI, AI Agents, Computer Vision. VP, Research @Samsung @Samsung_RA, Adjunct Professor @Stanford @StanfordSVL.

Stanford University niebles.net Joined April 2012

430 Following 5.6K Followers 964 Posts

Latest posts 5

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Juan Carlos Niebles @jcniebles Sep 9

In case you missed it, here are my slides for my talk today at CONTEXTUS @eccvconf

niebles.net/blog/2026/eccv…

Thanks to the organizer for the invitation and the audience for the engaging questions!

Juan Carlos Niebles @jcniebles Sep 9

📢I’ll be speaking at the CONTEXTUS workshop @eccvconf 2026 in just a few minutes! 1:20PM

📍Join us at Skybox 504-505 (5th floor, yes) at Malmo Arena.

I’ll be taking about our recent work on perception, evidence and efficiency for AI in the physical world.

#ECCV2026

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Juan Carlos Niebles @jcniebles Sep 10

Congrats @jcjohnss @AlexAlahi @drfeifei ! 🏆🎉
#ECCV2026 @StanfordAILab @StanfordHAI @StanfordSVL

European Conference on Computer Vision #ECCV2026 @eccvconf Sep 10

#ECCV2026 Test of Time Award winners

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Juan Carlos Niebles @jcniebles Sep 9

📢I’ll be speaking at the CONTEXTUS workshop @eccvconf 2026 in just a few minutes! 1:20PM

📍Join us at Skybox 504-505 (5th floor, yes) at Malmo Arena.

I’ll be taking about our recent work on perception, evidence and efficiency for AI in the physical world.

#ECCV2026

Juan Carlos Niebles @jcniebles Aug 24

I'm hiring! Looking for a Director of Vision Intelligence to join my team at @Samsung_RA 🚀

If you’re passionate about multimodal AI, computer vision, and bringing advanced research directly onto everyday devices, let’s connect!

Details & apply:

sra.samsung.com/careers/detail…

Juan Carlos Niebles @jcniebles Jun 2

Processing long videos with VLMs shouldn't scale quadratically. Enter StateKV! 🎬💡

By framing streaming prefill as a fixed-capacity temporal state, we unlock linear-time prefill while keeping full per-frame detail.

Paper by @CristbalEyzagu2 and team👇

arxiv.org/abs/2605.31598

arxiv.org Linear Scaling Video VLMs for Long Video Understanding Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compute and latency...

Cristóbal Eyzaguirre Ercilla Verified account @CristbalEyzagu2 Jun 2

1/ The biggest problem in video understanding today isn't the models. It's that we can barely run them.

Introducing StateKV: an inference-time method that makes pretrained video VLMs scale linearly with video length.🧵

🔗 ceyzaguirre4.github.io/StateKV

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