Most deep multi-view subspace clustering (DMVSC) methods usually employ pipeline optimization by learning the self-expression with a deep model first and then applying spectral clustering to group multi-view data. Subsequently, end-to-end DMVSC methods have been proposed by integrating these two steps into a unified optimization framework. However, the pipeline methods may suffer from misaligned clustering accumulation due to the noise or outlying entries, and the end-to-end methods often constitute a relatively complex parameter optimization. In this paper, we propose a novel method named decomposed deep multi-view subspace clustering with self-labeling supervision (D 2 MVSC) that runs with a decomposed optimization strategy by three-stage training. Specifically, multi-scale features are extracted by autoencoder in the pre-training stage. According to the discriminative contribution of each view, consensus self-expression is learned from these features by adaptive fusion and structure supervision to generate high-quality pseudo-labels in the fine-tuning stage. Finally, the pseudo-labels are used to retrain the model in a self-labeling supervision manner for robust clustering. Exciting, the self-labeling supervision can be used as an add-on module for other DMVSC methods to improve clustering performance. Extensive experiments on six datasets verify the effectiveness and superiority of our method over other state-of-the-art methods.

Tensor structures are widely utilized in clustering tasks due to their ability to represent high-order relationships among multi-view data. The rank and decomposition of tensors are essential characteristics that have recently received considerable attention. Most approaches approximate tensor rank using the tensor nuclear norm (TNN) and non-convex rank functions. We introduce a multi-view clustering method based on an enhanced self-expression with a tensor double arc-tangent norm. Initially leveraging tensor singular value decomposition , this method impartially approximates the true rank of the tensor, avoiding rank overestimation. The enhanced self-expression further utilizes the latent connections among the original data. Then, we propose an improved alternating direction method of multipliers (ADMM) for rapid optimization of the model. Experiments conducted on six datasets confirm the effectiveness of the proposed model.

Adversarial attacks in visual object tracking aim to drop tracking performance through injecting imperceptible perturbations to the input of the tracker. Current methods usually superimpose perturbation maps on the input images, and advocate blinding the tracker via occluding the real targets to achieve attack effect. From the perspective of attraction, we alternatively propose a novel idea of attacking the tracker, which advocates using perturbation patches to act as fake targets to attract the tracker's attention. For this purpose, we establish a multi-conditional objective function to generate our ideal patch in an offline iterative manner. For invisibility, we integrate the constraint of patch value into the function for unified optimization. For universality, in addition to adopting large-scale and high-diversity training samples , we also incorporate the video-agnostic condition into this function. To make the patch attractive like a fake target, we elaborately design the non-overlapping area to determine the patch position, and generate matched fake labels to mislead the tracker to track the patch. In online attacking, it only needs to paste the optimized patch onto the video frames, the tracker will be successfully attracted by our patch, achieving attack effect. Extensive experimental results on 8 popular tracking datasets demonstrate that our method can obtain exceptional attack performance in both non-targeted and targeted attack. Additionally, the experiments on transferability illustrate our optimized patches can be directly applied to other trackers with different architectures.

Subspace clustering plays an important role in the tasks such as data processing and pattern recognition. Since the high-dimensional data may contain complex noise, as well as non-linear structure, learning low-dimensional subspace structures is a challenging task. However, the existing methods to deal with both problems relax the original problem convexly. The results of solving by these methods deviate from the solution of the original problem. In this paper, to overcome this deficiency, we propose a robust low-rank kernel subspace clustering model, which coalesces the non-convex Schatten p-norm (0 <; p ≤ 1) regularizer with “kernel trick” and correntropy. Our “kernel trick” extends linear subspace clustering to non-linear counterparts, the Schatten p-norm regularizer can approximate the rank of the data in feature space effectively, and the correntropy is a robust measure to large corruptions. Furthermore, an efficient iterative algorithm (HQ-ADMM) is designed to solve the formulated problem, which coalesces the half-quadratic technique and Alternating Direction Method of Multipliers. This algorithm can ensure the closed form solutions at each iteration, which improves the computation speed of the algorithm. Extensive experiments on face/object clustering and motion segmentation clearly attest the ascendancy of the proposed method over several state-of-the-art methods.