The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
School of Data, Mathematical, and Statistical Sciences and the Department of Computer Science, University of Central Florida（数据、数学与统计科学学院和计算机科学系，中央佛罗里达大学）；School of Data, Mathematical, and Statistical Sciences, University of Central Florida（数据、数学与统计科学学院，中央佛罗里达大学）；Damo Academy, Alibaba US（阿里云美国研究院）
Handling The Non-Smooth Challenge in Tensor SVD: A Multi-Objective Tensor Recovery Framework
The University of British Columbia（不列颠哥伦比亚大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Qingdao Preschool Education College（青岛幼儿师范高等专科学校）；Zhejiang University of Technology（浙江工业大学）
Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
University of Electronic Science and Technology of China（电子科技大学）；Southwestern University of Finance and Economics（西南财经大学）；Peng Cheng Laboratory（鹏城实验室）；The University of Hong Kong（香港大学）；RIKEN Center on Advanced Intelligence Project（理化学研究所先进智能项目中心）
