Other authors include: Can Qin, Stefano Ermon, Yun Fu GlueGen was accepted by ICCV. In the rapidly advancing field of text-to-image synthesis, the remarkable progress in generating lifelike images from textual prompts has been evident. However, a significant challenge remains: how can we seamlessly integrate powerful pre-trained text encoders into

TLDR We trained a series of 7B LLMs named XGen-7B with standard dense attention on up to 8K sequence length for up to 1.5T tokens. We also fine tune the models on public-domain instructional data. The main take-aways are: * On standard NLP benchmarks, XGen achieves comparable or better results

AUTHORS: Chen Xing, Mingfei Gao, Donald Rose TL;DR: Most AI object detection methods work only on limited object categories, due to the human effort required for bounding-box annotations of training data. We developed a new method that automatically generates pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs,