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Human action recognition which recognizes human actions in a video is a fundamental task in computer vision field. Although multiple existing methods with single-view or multi-view have been presented for human action recognition, these recognition approaches cannot be extended into new action recognition or action classification tasks, as well as discover underlying correlations among different views. To tackle the above problem, this paper proposes a new lifelong multi-view subspace learning framework for continuous human action recognition, which could exploit the complementary information amongst different views from a lifelong learning perspective. More specifically, a set of view-specific libraries is established to gradually store the useful information within multiple views. As a new action recognition task comes, we decompose the model parameters into a set of embedded parameters over view-specific libraries. A latent representation subspace is constructed via encouraging it to be close to different view-specific libraries, which can leverage the high-order correlations among different views and further avoid partial information for action recognition task. Meanwhile, we propose to employ an alternating direction strategy to optimize our proposed method. Empirical studies on real-world multi-view action recognition datasets have shown that our proposed framework attains the superior recognition performance and saves the computational time when continually learning new action recognition tasks. [ABSTRACT FROM AUTHOR]

Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorate their performances: (1) the influences of noisy or irrelevant features in the original feature space are ignored, which may unexpectedly hurt the classification of target samples; (2) the graph constructed directly in the original data space cannot accurately capture the inherent local manifold structures of high-dimensional data due to the curse of dimensionality, which may seriously mislead the transferable features learning. In this paper, we propose a novel approach to address these problems, referred to as joint Adaptive Dual Graph and Feature Selection for domain adaptation (ADGFS). Specifically, feature selection can characterize the relative importance of different features through a scaling factor, which enables ADGFS to not only reduce the impacts of noisy or irrelevant features on knowledge transfer but also learn informative domain-invariant features. Meanwhile, ADGFS adaptively optimizes the dual graph by learning the similarity matrices of both instance-level and feature-level graphs in the projected low-dimensional manifold subspace rather than the original high-dimensional space, such that the intrinsic local manifold structures of data can be captured precisely. Moreover, ADGFS simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework to narrow the distribution discrepancies between the two different domains, which can adequately transfer knowledge from the source domain to the target domain. Comprehensive experiments on four benchmark datasets can demonstrate that the effectiveness of the proposed approach in cross-domain image classification. [ABSTRACT FROM AUTHOR]

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight wide-range transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets. [ABSTRACT FROM AUTHOR]

Metric learning has attracted significant attention in the past decades, because of its appealing advances in various real-world tasks, e.g., person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm through fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, the low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, the fast low-rank representation is implemented to mitigate the computational burden and ensure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, image clustering, and person re-identification. The experimental results verify the effectiveness of our proposed algorithm in comparison to many metric learning algorithms, even deep learning ones. [ABSTRACT FROM AUTHOR]

The graph-based subspace segmentation technique has garnered a lot of attention in the visual data representation problem. In general, data (e.g., tracks of moving objects) are drawn from multiple linear subspaces. Thus, how to build a block-diagonal affinity matrix is the critical problem. In this paper, we propose a novel graph-based method, Ensemble Subspace Segmentation under Blockwise constraints (ESSB), which unifies least squares regression and a locality preserving graph regularizer into an ensemble learning framework. Specifically, compact encoding using least squares regression coefficients helps achieve a block-diagonal representation matrix among all samples. Meanwhile, the locality preserving regularizer tends to capture the intrinsic local structure, which further enhances the block-diagonal property. Both the blockwise efforts, i.e., least squares regression and the sparse regularizer, work jointly and are formulated in the ensemble learning framework, making ESSB more robust and efficient, especially when handling high-dimensional data. Finally, an efficient optimization solution based on inexact augmented Lagrange multiplier is derived with theoretical time complexity analysis. To demonstrate the effectiveness of the proposed method, we consider three different applications: face clustering, object clustering, and motion segmentation. Extensive results of both accuracy and normalized mutual information on four benchmarks, i.e., YaleB, ORL, COIL and Hopkins155, are reported. Also, the evaluations of computational cost are provided, based on which the superiority of our proposed method in both accuracy and efficiency is demonstrated compared with 12 baseline algorithms. [ABSTRACT FROM AUTHOR]