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With the advent of vast data collection ways, data are often with multiple modalities or coming from multiple sources. Traditional multiview learning often assumes that each example of data appears in all views. However, this assumption is too strict in some real applications such as multisensor surveillance system, where every view suffers from some data absent. In this article, we focus on how to classify such incomplete multiview data in semisupervised scenario and a method called absent multiview semisupervised classification (AMSC) has been proposed. Specifically, partial graph matrices are constructed independently by anchor strategy to measure the relationships among between each pair of present samples on each view. And to obtain unambiguous classification results for all unlabeled data points, AMSC learns view-specific label matrices and a common label matrix simultaneously. AMSC measures the similarity between pair of view-specific label vectors on each view by partial graph matrices, and consider the similarity between view-specific label vectors and class indicator vectors based on the common label matrix. To characterize the contributions of different views, the $p$ th root integration strategy is adopted to incorporate the losses of different views. By further analyzing the relation between the $p$ th root integration strategy and exponential decay integration strategy, we develop an efficient algorithm with proved convergence to solve the proposed nonconvex problem. To validate the effectiveness of AMSC, comparisons are made with some benchmark methods on real-world datasets and in the document classification scenario as well. The experimental results demonstrate the advantages of our proposed approach.

Scene graph generation (SGG) is one of the hottest topics in computer vision and has attracted many interests since it provides rich semantic information between objects. In practice, the SGG datasets are often dual imbalanced, presented as a large number of backgrounds and rarely few foregrounds, and highly skewed foreground relationships categories (i.e., the long-tailed distribution). How to tackle this dual imbalanced problem is crucial but rarely studied in literature. Existing methods only consider the long-tailed distribution of foregrounds classes and ignore the background-foreground imbalance in SGG, which results in a biased model and prevents it from being applied in the downstream tasks widely. To reduce its side effect and make the contributions of different categories equally, we propose a novel debiased SGG method (named DSDI) by incorporating biased resistance loss and causal intervention tree. We first deeply analyze the potential causes of dual imbalanced problem in SGG. Then, to learn more discriminate representation of the foreground by expanding the foreground features space, the biased resistance loss decouples the background classification from foreground relationship recognition. Meanwhile, a causal graph of content and context is designed to remove the context bias and learn unbiased relationship features via casual intervention tree. Extensive experimental results on two extremely imbalanced datasets: VG150 and VrR-VG, demonstrate our DSDI outperforms other state-of-the-art methods. All our models will be available in https://github.com/zhouhao0515/unbiasedSGG-DSDI.