个人简介：

方力，博士，现任中国传媒大学媒体融合与传播国家重点实验室副研究员，硕士生导师。主要研究领域为三维视觉，沉浸式媒体技术。

报告摘要：

视频抠像、补全、调和是视频处理领域的关键技术，对视频编辑、电影制作、虚拟现实等行业都极为关键。确保视频序列的空间和时间一致性是这些任务的核心。有鉴于此，我们针对任务需要设计了基于光流和局部注意力的特征传播和对齐的有效方法。在此基础上，我们构建了视频虚拟化制作引擎，有力支持了AI技术在艺术创作中的应用。

相关专题好文：

Deep Video Harmonization by Improving Spatial-temporal Consistency

报告三：Exploring Generative Music Explainable AI

Nick Bryan-Kinns is Professor of Creative Computing at the Creative Computing Institute, University of the Arts London. He is a Distinguished Visiting Professor at Hunan University, China, a Fellow of the Royal Society of Arts, Fellow of the British Computer Society, and Senior Member of the Association of Computing Machinery. Bryan-Kinns has published award winning international journal and conference papers on Human Centred AI, explainable AI and Music, co-design, and interactive art. His research is reported widely in publications such as the New Scientist, and media outlets such as BBC, and exhibited at venues such as the Science Museum, London.

报告摘要：

Generative AI models for music and the arts in general are increasingly complex and hard to understand. The field of eXplainable AI (XAI) seeks to make complex and opaque AI models such as neural networks more understandable to people. One approach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on generative AI models. This talk introduces a systematic examination of the impact that different combinations of Variational Auto-Encoder models (MeasureVAE and AdversarialVAE), configurations of latent space in the AI model (from 4 to 256 latent dimensions), and training datasets (Irish folk, Turkish folk, Classical, and pop) have on explainable features of AI music generation. To date there have been no systematic comparisons of such models at this level of combinatorial detail.

相关专题好文：

Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI

 观看指南

直播通道①：中国科学院自动化所B站

哔哩哔哩APP搜索"中国科学院自动化所”或扫描下方二维码，直播当天点击头像即可进入

直播通道②：中国科学院自动化所视频号

扫描下方二维码预约观看

直播通道③：MIR视频号

扫描下方二维码预约观看

直播通道④：蔻享学术

扫描下方二维码进入MIR主题页，直播当天选择相应直播即可观看