报告题目: Making Sense of Massive Data

报告人: 沈春华

主持人: 欧林林

报告时间: 2023年12月26日 16：00-17：00

报告地点: 信息楼D533

报告摘要：

Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. We design a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. The synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.

报告人简介

沈春华，2021年入职浙江大学，担任计算机辅助设计与图形系统全国重点实验室副主任、浙江大学求是讲席教授。入选国家海外高层次人才计划、以及教育部长江讲座教授、担任科技部2030人工智能重大项目首席科学家。获得2019 Pattern Recognition 期刊最佳论文奖、2021 IEEE CVPR最佳论文提名。 谷歌学术引用62000余次，H index 117.