In most multi-modal weakly supervised video anomaly detection (WSVAD) methods, cross-modal alignment relies on cosine similarity measure, which captures only directional consistency but neglects feature magnitude information. Moreover, the existing multi-instance learning frameworks usually adopt the Top-k selection strategy, which is difficult to adapt to the anomalous proportions of different videos, resulting in missed detection of anomalous segments and introduction of label noise. To address these problems, an alignment and selection enhanced WSVAD (ASE-WSVAD) method is proposed. ASE-WSVAD combines cross-modal alignment method based on the fused similarity measure (CA-FSM) with dual-scale adaptive selection (DSAS) to improve semantic consistency and detection performance. Specifically, visual-textual alignment is implemented by a fusion of complementary similarity measures (i.e., cosine and Euclidean distance similarity measures) so that the alignment objective jointly leverages both direction and magnitude. DSAS combines local instance-level selection (LILS) with global batch-aware selection (GBAS) to efficiently detect anomalous segments and handle videos with varying proportions of anomalies. Experimental results demonstrate that ASE-WSVAD achieves the state-of-the-art performance with AUC value of 89.00% and AP value of 85.44% on UCF-Crime and XD-Violence, respectively.

Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. While PageRank and its variants show a good solution in related objects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, compatibility of linearity and nonlinearity, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [2] and [6], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic Bézier curve with control points restricted in the interior of a hypercube, complying with all the five meta-rules to infer a reasonable ranking list. With control points as model parameters, one is able to understand the learned manifold and to interpret and visualize the ranking results. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists.

Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related objects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, compatibility of linearity and nonlinearity, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [12] and [35], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic Bezier curve with control points restricted in the interior of a hypercube, complying with all the five meta-rules to infer a reasonable ranking list. With control points as model parameters, one is able to understand the learned manifold and to interpret and visualize the ranking results. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists.