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Our new paper, "Spectral Image Tokenizer", is on arXiv! We train a tokenizer on DWT coefficients that enables autoregressive coarse-to-fine image generation, w/ applications to multiscale text-to-image, and text-guided editing. w/ @kiamada, @msuhail153 arxiv.org/abs/2412.09607
We can't wait to welcome Carlos Esteves, Research Scientist at Google, tomorrow, January 22nd for a session on "Spectral Image Tokenizer." 🗓️ Learn more and add this event to your calendar: cohere.com/events/cohere-…
Don't miss the upcoming session on "Spectral Image Tokenizer" presented by @_machc, Research Scientist at @Google, on Wednesday January 22nd! Huge thanks to @AhmadMustafaAn1 for coordinating this event! 💫 Learn more: cohere.com/events/cohere-…
Don't miss the upcoming session on "Spectral Image Tokenizer" presented by @_machc, Research Scientist at @Google, on Wednesday January 22nd! Huge thanks to @AhmadMustafaAn1 for coordinating this event! 💫 Learn more: cohere.com/events/cohere-…
Our Equivariant Vision workshop features five great speakers @erikjbekkers @HaggaiMaron @ninamiolane @_machc, and Leo Guibas, spotlight talks, posters, and a tutorial prepared for the vision audience. Come tomorrow, Tuesday, at 8:30am in Summit 321! Thank you @CongyueD for…
I've never used this website for this but let's try: I'm on the lookout for full-time positions. The more research it involves the better. Open to both industrial and academic positions. If you know of good openings, my DMs are open, and I'll send my CV! PS: I'm on an O1-A visa.
At #CVPR2024, I will give a talk about "Geometric Deep Learning for Weather" at the Equivariant Vision workshop Tue 2pm equivision.github.io, and I'll present a poster on Single Mesh Diffusion Wed 5pm single-mesh-diffusion.github.io w/ @twmitchel and @kiamada. Hope to see you there!
After 3 years, it's time for us to start sharing the chapters of the GDL book! ❤️ Also included: companion slides from our @Cambridge_Uni & @UniofOxford courses 🧑🎓 Chapter 1 is out **now**! More to follow soon 🎉 geometricdeeplearning.com/book 📖 @mmbronstein @joanbruna @TacoCohen
Generate high quality textures with single mesh LDMs! #CVPR2024 Our *intrinsic* 3D diffusion models, trained on a single mesh, can generate texture variations, perform inpainting, and even transfer textures to different shapes. single-mesh-diffusion.github.io w/@twmitchel & @_machc
Our workshop "Equivariant Vision: From Theory to Practice" will be hosted at #CVPR2024 in Seattle this summer! @CVPR Both original and published works are welcome to submit to our workshop! 🔗equivision.github.io ⏰Deadline: Mar 22, 2024
We proudly present our 524 page book on equivariant convolutional networks. Coauthored by Patrick Forré, @erikverlinde and @wellingmax. maurice-weiler.gitlab.io/#cnn_book [1/N]
![maurice_weiler's tweet image. We proudly present our 524 page book on equivariant convolutional networks.
Coauthored by Patrick Forré, @erikverlinde and @wellingmax.
maurice-weiler.gitlab.io/#cnn_book
[1/N]](https://pbs.twimg.com/media/F8tveafWoAANtjt.jpg)
Some well-rounded results: @GoogleResearch work shows that deep learning on a sphere -- instead of flat space -- is superior for things like prediction of weather & molecular properties. Consider spherical surfaces (much better than pretending the world is flat!). See JAX code!
Applying computer vision models designed for planar images to data projected on spherical surfaces is challenging. Here we present an open-source library in JAX to solve the challenges of rotation and regular sampling for state-of-the-art performance → goo.gle/46z3vD7
Our blog post on scaling spherical CNNs for scientific applications was just published, check it out!
Applying computer vision models designed for planar images to data projected on spherical surfaces is challenging. Here we present an open-source library in JAX to solve the challenges of rotation and regular sampling for state-of-the-art performance → goo.gle/46z3vD7
Excited to announce our ICCV’23 paper: “Learning to Transform for Generalizable Instance-wise Invariance”! sutkarsh.github.io/projects/learn…
📢 Join us as we present ASIC at #ICCV2023 on Wed! We propose a method for dense correspondence that DOES NOT need tons of data/3D priors/manual annotations! How do we do it? Check out the 🧶 and visit Oral: Wed 4:30-6:00 PM Poster: Wed 2:30-4:30 PM Web: github.com/kampta/asic
ASIC: Aligning Sparse in-the-wild Image Collections abs: arxiv.org/abs/2303.16201 project page: kampta.github.io/asic/
The weather forecast is improving… literally! Introducing WeatherBench 2, a benchmark for the next generation of data-driven, global weather forecast models, providing data, tools, & an evaluation platform. Learn how to use it and check out the website →goo.gle/3YVUGAU
I'll present "Scaling Spherical CNNs" at #ICML2023 today 11am, Exhibit Hall 1 #215. Happy to chat anytime during the conference!
Scaling Spherical CNNs paper page: huggingface.co/papers/2306.05… Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain…
Scaling Spherical CNNs paper page: huggingface.co/papers/2306.05… Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain…
Applications for the first-ever Google PhD Fellowships for students in Latin America open today, along with applications to support early-career professors through Research Scholar. Read more about our investments in the Latin American research ecosystem ↓goo.gle/3Gz3bKU
Ameesh Makadia’s first vision conference was ECCV 2002 in Copenhagen. So proud to see him present their “Generalized patch-based rendering” with our Carlos @_machc, Sigal and Suhail at #eccv2022 today.
In this Google AI Blog post, we summarize our recent contributions on neural rendering with transformers. Check it out! @kiamada @msuhail153
View synthesis — creating new views of a scene from multiple pictures of it — can be challenging because models need to capture many types of information from a small set of images. Learn how two transformer-based models improve on the state of the art ↓goo.gle/3ByUKvc
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