Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a $l_{1}$ norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC .

The continuous developments of urban and industrial environments have increased the demand for intelligent video surveillance. Deep learning has achieved remarkable performance for anomaly detection in surveillance videos. Previous approaches achieve anomaly detection with a single-pretext task (image reconstruction or prediction) and detect anomalies by larger reconstruction error or poor prediction. However, they cannot fully exploit the discriminative semantics and temporal context information. Moreover, tackling anomaly detection with a single pretext task is suboptimal due to the nonalignment between the pretext task and anomaly detection. In this article, we propose a temporal-aware contrastive network (TAC-Net) to address the abovementioned problems of anomaly detection for intelligence video surveillance. TAC-Net is an unsupervised method that utilizes deep contrastive self-supervised learning to capture the high-level semantic features and tackles anomaly detection with multiple self-supervised tasks. During inference phase, the multiple task losses and contrastive similarity are utilized to calculate the anomaly score. Experimental results show that our method is superior to state-of-the-art approaches on three benchmarks, which demonstrates the validity and advancement of TAC-Net.

Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos. The deep generative model (DGM)-based method learns the regular patterns on normal videos and expects the learned model to yield larger generative errors for abnormal frames. However, DGM cannot always do so, since it usually captures the shared patterns between normal and abnormal events, which results in similar generative errors for them. In this article, we propose a novel self-supervised framework for unsupervised VAD to tackle the above-mentioned problem. To this end, we design a novel self-supervised attentive generative adversarial network (SSAGAN), which is composed of the self-attentive predictor, the vanilla discriminator, and the self-supervised discriminator. On the one hand, the self-attentive predictor can capture the long-term dependences for improving the prediction qualities of normal frames. On the other hand, the predicted frames are fed to the vanilla discriminator and self-supervised discriminator for performing true–false discrimination and self-supervised rotation detection, respectively. Essentially, the role of the self-supervised task is to enable the predictor to encode semantic information into the predicted normal frames via adversarial training, in order for the angles of rotated normal frames can be detected. As a result, our self-supervised framework lessens the generalization ability of the model to abnormal frames, resulting in larger detection errors for abnormal frames. Extensive experimental results indicate that SSAGAN outperforms other state-of-the-art methods, which demonstrates the validity and advancement of SSAGAN.

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multiview data. To date, existing IMC methods usually bypass unavailable views according to prior missing information, which is considered a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this article, we propose an information-recovery-driven-deep IMC network, termed as RecFormer. Concretely, a two-stage autoencoder network with self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote representation learning and further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.

Weakly supervised video anomaly detection is generally formulated as a multiple instance learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly scores under the supervision of MIL-based video-level classification. However, most previous works suffer from two drawbacks: 1) they lack ability to model temporal relationships between video segments and 2) they cannot extract sufficient discriminative features to separate normal and anomalous snippets. In this article, we develop a weakly supervised temporal discriminative (WSTD) paradigm, that aims to leverage both temporal relation and feature discrimination to mitigate the above drawbacks. To this end, we propose a transformer-styled temporal feature aggregator (TTFA) and a self-guided discriminative feature encoder (SDFE). Specifically, TTFA captures multiple types of temporal relationships between video snippets from different feature subspaces, while SDFE enhances the discriminative powers of features by clustering normal snippets and maximizing the separability between anomalous snippets and normal centers in embedding space. Experimental results on three public benchmarks indicate that WSTD outperforms state-of-the-art unsupervised and weakly supervised methods, which verifies the superiority of the proposed method.

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable since the deep AE usually captures the low-level shared features between normal and abnormal data, which leads to similar reconstruction errors for them. To tackle this problem, we propose a self-supervised representation-augmented deep AE for unsupervised VAD, which can enlarge the gap of anomaly scores between normal and abnormal samples by introducing autoencoding transformation (AT). Essentially, AT is introduced to facilitate AE to learn the high-level visual semantic features of normal images by introducing a self-supervision task (transformation reconstruction). In particular, our model inputs the original and transformed images into the encoder for obtaining latent representations; afterward, they are fed to the decoder for reconstructing both the original image and applied transformation. In this way, our model can utilize both image and transformation reconstruction errors to detect anomaly. Extensive experiments indicate that the proposed method outperforms other state-of-the-art methods, which demonstrates the validity and advancement of our model.