The computer vision and reasoning lab in the Allen School at the University of Washington, led by Ali Farhadi and Ranjay Krishna.
Seattle, WA
Joined April 2020
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UW RAIVN Lab retweeted
📢Applications are open for summer'25 internships at the PRIOR (computer vision) team
@allen_ai:
Come join us in building large-scale models for:
📸 Open-source Vision-Language Models
💻 Multimodal Web Agents
🤖 Embodied AI + Robotics
🌎 Planet Monitoring
Apply by December 11, 2024!
Congrats [Dr. [Dr. Aditya] Kusupati]!!🪆🪆
This (& graduation) happened last week & I am a (fake) Dr. now!
I owe it all to my advisors, mentors, collaborators, friends, and family! -- I wrote a 6-page acknowledgment in my thesis without realizing😅
Thanks for all the fish @uwcse, @RAIVNLab, @uw_wail & @GoogleDeepMind🪆
Very cool to see our RAIVN lab alum Rowan in the latest GPT-4o demo!!🚀🚀
UW RAIVN Lab retweeted
🎉 Very Excited to present our recent work on “Selective🔍 Visual Representations for Embodied-AI🤖” next week at ICLR in Vienna🇦🇹!!
📣📣Important update! Our code and pretrained models are now available through our project website 🌐: embodied-codebook.github.io/🚀
👋Come to my poster, say hi, and learn more about our findings! (Poster #111, Session 8, on Friday, May 10th at 4:30 PM)
Embodied-AI 🤖 models employ general-purpose vision backbones such as CLIP to encode the observation. How can we have a more task-driven visual perception for embodied-AI?
We introduce a parameter-efficient approach that selectively filters visual representations for Embodied-AI tasks.
Project page: embodied-codebook.github.io
🧵👇
Check out🪆MatFormer🪆co-led by @adityakusupati: it’s a simple yet powerful general-purpose architecture with flexibility and elasticity built within. It works across modalities and enables super cool things at web-scale tasks🔥🔥
Announcing MatFormer - a nested🪆(Matryoshka) Transformer that offers elasticity across deployment constraints.
MatFormer is an architecture that lets us use 100s of accurate smaller models that we never actually trained for!
arxiv.org/abs/2310.07707 1/9
Exciting work with open source code to facilitate research on medium sized language models! 🎉
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Check out this cool work led by @DJiafei on collecting robot data without a robot! 🤖🦾
🚨Is it possible to devise an intuitive approach for crowdsourcing trainable data for robots without requiring a physical robot🤖?
Can we democratize robot learning for all?🧑🤝🧑
Check out our latest #CoRL2023 paper->
AR2-D2: Training a Robot Without a Robot
If you are at #CVPR2023, come check out prompting-in-vision.github.i… on Monday, June 19 from 9am - 12pm in West room 223-224. Speakers include @sarahmhpratt from RAIVN lab as well as @liuziwei7 @phillip_isola @hyojinbahng @lschmidt3 and @denny_zhou!
We're organizing a tutorial on Prompting in Vision at #CVPR2023 w/ @liuziwei7 @phillip_isola @hyojinbahng @lschmidt3 @sarahmhpratt @denny_zhou
Please visit our website at prompting-in-vision.github.i… to know more about this event
Checkout CREPE led by @zixianma02, a new large-scale benchmark for vision-language model!!
Drop by the poster at CVPR 2023.
Have vision-language models achieved human-level compositional reasoning? Our research suggests: not quite yet.
We’re excited to present CREPE – a large-scale Compositional REPresentation Evaluation benchmark for vision-language models – as a 🌟highlight🌟at #CVPR2023.
🧵1/7
Check out our new work on improving large-scale nearest-neighbor search!
Introducing💃AdANNS: A Framework for Adaptive Semantic Search🕺
TL;DR: Up to 90× faster nearest neighbor retrieval and 2× lower memory cost for web-scale search.
Applies to vector search at scale & improves all "retrieval" augmented models!
arxiv.org/abs/2305.19435
[1/8]
UW RAIVN Lab retweeted
Introducing💃AdANNS: A Framework for Adaptive Semantic Search🕺
TL;DR: Up to 90× faster nearest neighbor retrieval and 2× lower memory cost for web-scale search.
Applies to vector search at scale & improves all "retrieval" augmented models!
arxiv.org/abs/2305.19435
[1/8]
UW RAIVN Lab retweeted
1/9 I am excited to announce that our workshop "Towards the Next Generation of Computer Vision Datasets" will be happening at ICCV 2023 in Paris. We will feature DataComp submissions, other data-centric papers, and invited talks by experts. datacomp.ai/workshop
Excited to announce DataComp! Let us create stronger models using better-filtered data.
Introducing DataComp, a new benchmark for multimodal datasets!
We release 12.8B image-text pairs, 300+ experiments and a 1.4B subset that outcompetes compute-matched CLIP runs from OpenAI & LAION
📜 arxiv.org/abs/2304.14108
🖥️ github.com/mlfoundations/dat…
🌐 datacomp.ai
UW RAIVN Lab retweeted
Introducing DataComp, a new benchmark for multimodal datasets!
We release 12.8B image-text pairs, 300+ experiments and a 1.4B subset that outcompetes compute-matched CLIP runs from OpenAI & LAION
📜 arxiv.org/abs/2304.14108
🖥️ github.com/mlfoundations/dat…
🌐 datacomp.ai