Jialuo Li

🚀 Introducing Science-T2I - Towards bridging the gap between AI imagination and scientific reality in image generation! [CVPR 2025] 📜 Paper: arxiv.org/abs/2504.13129 🌐 Project: jialuo-li.github.io/Science-T2I-Web 💻 Code: github.com/Jialuo-Li/Scie… 🤗 Dataset: huggingface.co/collections/Ji… 🔍…

Over 4 years into our journey bridging Convolutions and Transformers, we introduce Generalized Neighborhood Attention—Multi-dimensional Sparse Attention at the Speed of Light: github.com/SHI-Labs/NATTEN A collaboration with the best minds in AI and HPC. 🐝🟩🟧 @gtcomputing @nvidia

This paper is interestingly thought- provoking for me. There is a chance, that it's easier to "align t2i model with real physics" in post-training. And let it learn to generate whatever (physically implausible) combinations in pretrain. As opposed to trying hard to come up with…

Embedding a scientific basis in pre-trained T2I models can enhance the realism and consistency of the results. Cool work in "Science-T2I: Addressing Scientific Illusions in Image Synthesis" jialuo-li.github.io/Science-T2I-We…

In Cambrian-1, we found that vision SSL representations usually lagged behind language-supervised ones -- but once the data gap is closed and scaling kicks in, performance catches up. We’ve tried scaling SSL before, but this is the first time I’ve seen real signal: SSL adapts to…