Ryousuke Yamada

Research Scientist @ AIST | Visiting Postdoc @ UTN | HQ @ cvpaper.challenge

[Takayuki Shinohara @shnhrtkyk]

🎉 Thrilled to share that our paper received the Best Paper Award at the CV4AEC at CVPR 2026!
Huge thanks to my co-authors, the organizers, and the entire CVPR community! 🙏
#CVPR2026 #CV4AEC

[Ryousuke Yamada @FragileGoodwill]

Matt Deitke is presenting now in VGI workshop 😀

[Ryousuke Yamada @FragileGoodwill]

Our Workshop will take place on June 3, 8:30 AM in Room 703😃

With a great lineup of speakers, we look forward to discussing:
What is Visual General Intelligence?

Let’s think together about the future of the field!!
Please join!!

#CVPR2026 @CVPR

[Ryousuke Yamada @FragileGoodwill]

“Video models = Visual Foundation Models”

Video models today may be where LLMs were a few years ago 🤔

[Ryousuke Yamada @FragileGoodwill]

Our Workshop will take place on June 3, 8:30 AM in Room 703😃

With a great lineup of speakers, we look forward to discussing:
What is Visual General Intelligence?

Let’s think together about the future of the field!!
Please join!!

#CVPR2026 @CVPR

[Ryousuke Yamada @FragileGoodwill]

VGI workshop just started at Room 703 😀

#CVPR2026

[Ryousuke Yamada @FragileGoodwill]

Our Workshop will take place on June 3, 8:30 AM in Room 703😃

With a great lineup of speakers, we look forward to discussing:
What is Visual General Intelligence?

Let’s think together about the future of the field!!
Please join!!

#CVPR2026 @CVPR

[Ryousuke Yamada @FragileGoodwill]

📣📣📣
BigMac Workshop Room changed "Room Four Seasons 2"

Please join to us!!!!

[Ryousuke Yamada @FragileGoodwill]

How can we effectively adapt and leverage foundation models in computer vision?

At the BigMac Workshop, we will explore research directions in the era of foundation models through talks by an amazing lineup of speakers!!

📅 June 4, 1:00 PM
📍 Room 2E-2H

#CVPR2026 @CVPR

[Ryousuke Yamada @FragileGoodwill]

How can we effectively adapt and leverage foundation models in computer vision?

At the BigMac Workshop, we will explore research directions in the era of foundation models through talks by an amazing lineup of speakers!!

📅 June 4, 1:00 PM
📍 Room 2E-2H

#CVPR2026 @CVPR

[Takuma Yagi (八木 拓真) Ph.D.@CVPR @takuma_yagi_]

We will present our work on fine-grained Hand-Object Interaction understanding VideoQA benchmark (HanDyVQA) at the #CVPR2026 main track! Say hello at
- #EgoVis Workshop Poster (3rd 15:30-16:15)
- Poster Session 1 (5th 10:45-12:45)

[Ryousuke Yamada @FragileGoodwill]

I’ll be at #CVPR2026 this week!

@AIST_EN will have a sponsor booth, and I’m looking forward to meeting many potential collaborators there😀

If you are interested in research opportunities in Japan, please stop by and say hello!!
📍 AIST Booth #816

#CVPR2026AIST @CVPR

[Ryousuke Yamada @FragileGoodwill]

How can we effectively adapt and leverage foundation models in computer vision?

At the BigMac Workshop, we will explore research directions in the era of foundation models through talks by an amazing lineup of speakers!!

📅 June 4, 1:00 PM
📍 Room 2E-2H

#CVPR2026 @CVPR

[Oishi Deb @DebOishi]

Checkout our @CVPR VGI workshop on 3rd Jun 2026 https://t.co/lSKkVLoXzp in Room 703, Led by @HirokatuKataoka with Yoshihiro Fukuhara, @YonglongT, @elliottszwu, Ryousuke Yamada, @jianyuan_wang, Kohsuke Ide, @xianzhengoxford, @Koichi_N_, @yimiche @Oxford_VGG! @CVPRConf #CVPR2026!!

[Ryousuke Yamada @FragileGoodwill]

Our Workshop will take place on June 3, 8:30 AM in Room 703😃

With a great lineup of speakers, we look forward to discussing:
What is Visual General Intelligence?

Let’s think together about the future of the field!!
Please join!!

#CVPR2026 @CVPR

[産業技術総合研究所（産総研） @AIST_JP]

【プレスリリース】
「1つの物体を見るAI」から「複数物体を見比べるAI」へ
～単一部品にとどまらず、部品同士の幾何的関係性まで説明可能な点群言語モデルを開発～
https://t.co/AUqYGoOe1O

✅従来の視覚言語モデルは単一物体の理解に留まっていたが、複数物体を見比べて「どの部品が接合するか」などまで理解し、説明できる点群言語モデルを開発
✅複数物体の幾何的関係性を学習する独自データセットとモデル設計により、部品同士の接合関係や形状変化について説明できることを実証
✅部品の接合判断は設計・製造の根幹工程であり、本技術は製造現場における専門家の判断の自動化・効率化に貢献することが期待される

[Ryousuke Yamada @FragileGoodwill]

CVPR2026発表予定の研究成果です！
複数点群物体に関する質問応答を可能とする3D-LLMを構築しました！

産総研RA/筑波大学 @IdeKohsuke が主著研究になります😀

プロジェクトページ
https://t.co/eTOG3N1l37

#CVPR2026 #CVPR2026AIST

[産業技術総合研究所（産総研） @AIST_JP]

【プレスリリース】
「1つの物体を見るAI」から「複数物体を見比べるAI」へ
～単一部品にとどまらず、部品同士の幾何的関係性まで説明可能な点群言語モデルを開発～
https://t.co/AUqYGoOe1O

✅従来の視覚言語モデルは単一物体の理解に留まっていたが、複数物体を見比べて「どの部品が接合するか」などまで理解し、説明できる点群言語モデルを開発
✅複数物体の幾何的関係性を学習する独自データセットとモデル設計により、部品同士の接合関係や形状変化について説明できることを実証
✅部品の接合判断は設計・製造の根幹工程であり、本技術は製造現場における専門家の判断の自動化・効率化に貢献することが期待される

[Yuki @y_m_asano]

🎉[openings] I’m hiring postdoctoral researchers to join our @FunAILab at UTN through the Alexander von Humboldt Research Fellowship (@AvHStiftung), via the Henriette Herz Scouting Programme.

As a Henriette Herz Scout, I can nominate outstanding international researchers for this fellowship route. I’m especially keen to hear from candidates working on multimodal learning, video and image pretraining, and post-training.

Fellows would be hosted in our lab at UTN and work closely with us on these topics.

Key requirements:
* finished your doctoral studies less than 4 years ago or will finish in the next 6 months
* did not live/work in Germany in the last 10 years
* applications from female, trans* and/or non-binary candidates are highly encouraged!

Interested? Please send a short note with your CV, PhD year, current affiliation, 2–3 key publications, and a few lines on how your work connects.

Please share! 🔀

[Yuki @y_m_asano]

[new CVPR'26 paper]
🔄 SSL works great when you have tons of data.
But in 3D… we don’t.
High-quality 3D scans are expensive, slow, and hard to scale. So what if we could pretrain 3D models without any real 3D scans? 1/

[Andrei Bursuc @CVPR @abursuc]

Can we get a SoTA point cloud encoder without actual 3D scans?
Yes, we can, thanks to a clever mix of:
- data generation: video point clouds from web videos
- LAM3C: a new self-supervised learning strategy for such data

Super fun #cvpr2026 project led by @FragileGoodwill
👇

[Hirokatsu Kataoka | 片岡裕雄 #CVPR2026 @HirokatuKataoka]

This work lies at the intersection of reconstruction × 3D point clouds (PC) × visual pre-training under limited data.
Video-generated PCs offer a scalable alternative for PC pre-training without (sans in French) relying on 3D scans.
https://t.co/89zvutBpfB

[Ryousuke Yamada @FragileGoodwill]

Super excited to share our project (LAM3C x RoomTours)!!

Can we learn 3D point cloud representations without real 3D scans?

Our work explores exactly this 👇
https://t.co/c6oLJ1BFNi

#CVPR2026 @CVPR

[Yuki @y_m_asano]

[new CVPR'26 paper]
🔄 SSL works great when you have tons of data.
But in 3D… we don’t.
High-quality 3D scans are expensive, slow, and hard to scale. So what if we could pretrain 3D models without any real 3D scans? 1/

[Seungwook Han @seungwookh]

Can language models learn useful priors without ever seeing language?

We pre-pre-train transformers on neural cellular automata — fully synthetic, zero language. This improves language modeling by up to 6%, speeds up convergence by 40%, and strengthens downstream reasoning.

Surprisingly, it even beats pre-pre-training on natural text!

Blog: https://t.co/Pni0RsIcxL

(1/n)

[Ryousuke Yamada @FragileGoodwill]

@XiaoyangWu_ @_akhaliq Amazing work😀
Thanks for this great research