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Abstract Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. changes in illumination conditions. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data‐driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. On both synthetic and real‐world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.

Abstract Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach is proposed through constructing the short circuit neural connections. The proposed short circuit is a unidirectional neural connection that back propagates the sensitivities rather than gradients in neural networks from the deep layers to the shallow layers. Moreover, the short circuit is further formulated as a gradient truncation operation in its connecting layers, which can be plugged into the backbone models without introducing extra training parameters. Extensive experiments demonstrate that the deep neural networks, with the help of short circuit connection, gain a large margin of improvement over the baselines on both computer vision and natural language processing tasks. The work provides the promising solution to the low‐resource scenarios, such as, intelligence transport systems of computer vision, question answering of natural language processing.

Despite much effort has been devoted to skin lesion segmentation, the performance of existing methods is still not satisfactory enough for practical applications. The challenges may include fuzzy lesion boundary, uneven and low contrast, and variation of colors across space, which often lead to fragmentary segmentation and inaccurate boundary. To alleviate this problem, we propose a multi-scale context-guided network named as MSCGnet to segment the skin lesions accurately. In MSCGnet, the context information is utilized to guide the feature encoding procedure. Moreover, because of the information loss in spatial down-sampling, a context-based attention structure (CAs) is designed to select effective context features in the decoding path. Furthermore, we boost the performance of MSCGnet with iterations and term this upgraded version as iterative MSCGnet, denoted as iMSCGnet. To supervise the training of iMSCGnet in an end-to-end fashion, a novel objective function of deep supervision, which consists of the terms of each encoding layers and the terms from each MSCGnet output of iMSCGnet, is employed. Our method is evaluated extensively on the four publicly available datasets, including ISBI2016 [1], ISBI2017 [2], ISIC2018 [3] and PH2 [4] datasets. The experimental results prove the effectiveness of proposed components and show that our method generally outperforms the state-of-the-art methods.

Graphene-based nanocomposite films (NCFs) are in high demand due to their superior photoelectric and thermal properties, but their stability and mechanical properties form a bottleneck. Herein, a facile approach was used to prepare nacre-mimetic NCFs through the non-covalent self-assembly of graphene oxide (GO) and biocompatible proteins. Various characterization techniques were employed to characterize the as-prepared NCFs and to track the interactions between GO and proteins. The conformational changes of various proteins induced by GO determined the film-forming ability of NCFs, and the binding of bull serum albumin (BSA)/hemoglobin (HB) on GO’s surface was beneficial for improving the stability of as-prepared NCFs. Compared with the GO film without any additive, the indentation hardness and equivalent elastic modulus could be improved by 50.0% and 68.6% for GO–BSA NCF; and 100% and 87.5% for GO–HB NCF. Our strategy should be facile and effective for fabricating well-designed bio-nanocomposites for universal functional applications.