Multiview Learning

A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available. Source: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

We provide here a new multi-view text dataset, collected from three well-known online news sources: BBC, Reuters, and The Guardian. This dataset exhibits a number of common aspects of multi-view problems highlighted previously -- notably that certain stories will not be reported by all three sources (i.e incomplete views), and the related issue that sources vary in their coverage of certain topics (i.e. partially missing patterns).

MNIST Multiview Datasets ======================== MNIST is a publicly available dataset consisting of 70, 000 images of handwritten digits distributed over ten classes. We generated 2 four-view datasets where each view is a vector of R^{14 x 14}: * MNIST₁: It is generated by considering 4 quarters of image as 4 views. * MNIST₂: It is generated by considering 4 overlapping views around the centre of images: this dataset brings redundancy between the views. Related Papers: Goyal, Anil, Emilie Morvant, Pascal Germain, and Massih-Reza Amini. "Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters." Neurocomputing, 358, 2019, pp. 81-92. Link to the ArXiv version: https://arxiv.org/abs/1808.05784 Published Version: https://doi.org/10.1016/j.neucom.2019.04.072 Goyal, Anil, Emilie Morvant, and Massih-Reza Amini. "Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization." In International Symposium on Intelligent Data Analysis, pp. 124-136. Springer, Cham, 2018. Link to the ArXiv version: https://arxiv.org/abs/1805.10212 Published Version: https://doi.org/10.1007/978-3-030-01768-2_11

Dataset for multimodal skills assessment focusing on assessing piano player’s skill level. Annotations include player's skills level, and song difficulty level. Bounding box annotations around pianists' hands are also provided.