报告主题：基于视觉大模型的步态识别方法：从小数据到大模型报告日期：03月15日 10:30-11:30
主题简介：步态识别是在无约束、远距离等挑战场景下能实现跨视角、跨外观识别个体身份的唯一一种生物特征识别技术。结合课题组发表在CVPR2024上的BigGait: Learning Gait Representations You Want by Large Vision Models的观点。本报告将围绕步态数据、步态表征以及步态应用三方面简述近期步态识别进展，具体涉及三个问题：1、步态数据采集难且分布失衡，如何利用无标注数据克服该挑战？2、RGB行人视频包含众多步态无关噪声，而主流分割所得剪影序列的信息又太稀疏，所以我们到底需要怎样的步态表征用于识别任务？3、除安防系统外，步态识别还有哪些用武之地？GitHub主页：https://github.com/ShiqiYu/OpenGait
报告嘉宾：
樊超，南方科技大学三年级博士生，主要研究方向为计算机视觉和深度学习。针对步态识别任务，他曾在CVPR和T-PAMI等CCF-A类顶会（含ECCV）和顶刊发表论文8篇，其中6篇为第一/共一作者，话题涉及步态识别+自监督学习、视觉大模型和生成式模型等，当前Google Scholar被引400余次。他主导开发了步态识别领域主流代码库OpenGait.Google Scholar主页：https://scholar.google.com/citations?user=lgDtKZcAAAAJ&hl=zh-CN扫描下方二维码
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