In the task of skeleton-based action recognition, long-term temporal dependencies are significant cues for sequential skeleton data. State-of-the-art methods rarely have access to long-term temporal information, due to the limitations of their receptive fields. Meanwhile, most of the recent multiple branches methods only consider different input modalities but ignore the information in various temporal scales. To address the above issues, we propose a multi-scale temporal transformer (MTT) in this letter, for skeleton-based action recognition. Firstly, the raw skeleton data are embedded by graph convolutional network (GCN) blocks and multi-scale temporal embedding modules (MT-EMs), which are designed as multiple branches to extract features in various temporal scales. Secondly, we introduce transformer encoders (TE) to integrate embeddings and model the long-term temporal pattern. Moreover, we propose a task-oriented lateral connection (LaC) aiming to align semantical hierarchies. LaC distributes input embeddings to the downstream transformer encoders (TE), according to semantical levels. The classification headers aggregate results from TE and predict the action categories at last. The proposed method is shown efficiency and universality during experiments and achieves the state-of-the-art on three large datasets, NTU-RGBD 60, NTU-RGBD 120 and Kinetics-Skeleton 400.

As one of the vital topics in intelligent surveillance, weakly supervised online video anomaly detection (WS-OVAD) aims to identify the ongoing anomalous events moment-to-moment in streaming videos, trained with only video-level annotations. Previous studies tended to utilize a unified single-stage framework, which struggled to simultaneously address the issues of online constraints and weakly supervised settings. To solve this dilemma, in this paper, we propose a two-stage-based framework, namely “decouple and resolve” (DAR), which consists of two modules, i.e., temporal proposal producer (TPP) and online anomaly localizer (OAL). With the supervision of video-level binary labels, the TPP module targets fully exploiting hierarchical temporal relations among snippets for generating precise snippet-level pseudo-labels. Then, given fine-grained supervisory signals produced by TPP, the Transformer-based OAL module is trained to aggregate both the useful cues retrieved from historical observations and anticipated future semantics, for making predictions at the current time step. Both the TPP and OAL modules are jointly trained to share the beneficial knowledge in a multi-task learning paradigm. Extensive experimental results on three public data sets validate the superior performance of the proposed DAR framework over the competing methods.

Currently, how to deal with the massive garbage produced by various human activities is a hot topic all around the world. In this paper, a preliminary and essential step is to classify the garbage into different categories. However, the mainstream waste classification mode relies heavily on manual work, which consumes a lot of labor and is very inefficient. With the rapid development of deep learning, convolutional neural networks (CNN) have been successfully applied to various application fields. Therefore, some researchers have directly adopted CNNs to classify garbage through their images. However, compared with other images, the garbage images have their own characteristics (such as inter-class similarity, intra-class variance and complex background). Thus, neglecting these characteristics would impair the classification accuracy of CNN. To overcome the limitations of existing garbage image classification methods, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed in this paper. In DSCAM, the inherent relationships of channels and spatial positions in garbage image features are captured by two attention modules with depth-wise separable convolutions, so that our method could only focus on important information and ignore the interference. Moreover, we also adopt a residual network as the backbone of DSCAM to enhance its discriminative ability. We conduct the experiments on five garbage datasets. The experimental results demonstrate that the proposed method could effectively classify the garbage images and that it outperforms some classical methods.