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ICIP 2020 is a fully virtual conference. The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website

Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. Recently, under the assumption that the leading canonical correlation directions are sparse, various procedures have been proposed for many high-dimensional applications to improve the interpretability of CCA. However all these procedures have the inconvenience of not preserving the sparsity among the retained leading canonical directions. To address this issue, a new sparse CCA method is proposed in this paper.

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The ability of deep neural networks to extract complex statistics and learn high level features from vast datasets is proven.Yet current deep learning approaches suffer from poor sample efficiency in stark contrast to human perception. Fewshot learning algorithms such as matching networks or ModelAgnostic Meta Learning (MAML) mitigate this problem, enabling fast learning with few examples. In this paper, we ex-tend the MAML algorithm to point cloud data using a Point-Net Architecture.

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Priors play an important role of regularizers in image deblurring algorithms. Image priors are frequently studied and many forms were proposed in the literature. Blur priors are considered less important and the most common forms are simple uniform distributions with domain constraints. We propose a more informative blur prior based on the notion of atomic norm which favors blurs composed of line segments and is suitable for motion blur. The prior is formulated as a linear program that can be inserted into any optimization task.

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Spatial-temporal graph convolutional networks (ST-GCN) have achieved outstanding performances on human action recognition, however, it might be less superior on a two-person interaction recognition (TPIR) task due to the relationship of each skeleton is not considered. In this study, we present an improvement of the ST-GCN model that focused on TPIR by employing the pairwise adjacency matrix to capture the relationship of person-person skeletons (ST-GCN-PAM). To validate the effectiveness of the proposed ST-GCN-PAM model on TPIR, experiments were conducted on NTU RGB+D 120.

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