Group activity recognition (GAR) is a challenging task for discerning the behavior of a group of actors. This paper aims at learning discriminative representation for GAR in a self-supervised manner based on human skeletons. As modeling relations between actors lie at the center of GAR, we propose a valid self-supervised learning pretext task with a matching framework, where a representation model is driven to identify subgroups in a synthetic group based on actors' skeleton sequences. For backbone networks, while spatial-temporal graph convolution networks have dominated the skeleton-based action recognition, they under-explore the group relevant interactions among actors. To address this issue, we come up with a novel plug-in Actor-Association Graph Convolution Module (AAGCM) based on inductive graph convolution, which can be integrated into many common backbones. It can not only model the interactions at different levels but also adapt to variable group sizes. The effectiveness of our approaches is demonstrated by extensive experiments on three benchmark datasets: Volleyball, Collective Activity, and Mutual NTU.

Skeleton data have been extensively used for action recognition since they can robustly accommodate dynamic circumstances and complex backgrounds. To guarantee the action-recognition performance, we prefer to use advanced and time-consuming algorithms to get more accurate and complete skeletons from the scene. However, this may not be acceptable in time- and resource-stringent applications. In this paper, we explore the feasibility of using low-quality skeletons, which can be quickly and easily estimated from the scene, for action recognition. While the use of low-quality skeletons will surely lead to degraded action-recognition accuracy, in this paper we propose a structural knowledge distillation scheme to minimize this accuracy degradations and improve recognition model's robustness to uncontrollable skeleton corruptions. More specifically, a teacher which observes high-quality skeletons obtained from a scene is used to help train a student which only sees low-quality skeletons generated from the same scene. At inference time, only the student network is deployed for processing low-quality skeletons. In the proposed network, a graph matching loss is proposed to distill the graph structural knowledge at an intermediate representation level. We also propose a new gradient revision strategy to seek a balance between mimicking the teacher model and directly improving the student model's accuracy. Experiments are conducted on Kenetics400, NTU RGB+D and Penn action recognition datasets and the comparison results demonstrate the effectiveness of our scheme.

Academic emotions can produce a great impact on the learning effect. Normally, emotions are expressed externally in the students' facial expressions, speech and behaviour. In this paper, the focus is on automatic academic emotion inference based on facial expressions in online learning. Considering the lack of training samples for the inference algorithm, a spontaneous facial expression database is established. It includes the facial expressions of five common academic emotions and consists of two subsets: a video clip database and an image database. A total of 1,274 video clips and 30,184 images from 82 students are included in the database. The samples are labelled by both the participants and external coders. An extensive analysis is carried out on the image database using a convolutional neural network (CNN)‐based algorithm to infer self‐annotation. Some data augmentation algorithms are applied to improve the algorithm performance. Additionally, an adaptive data augmentation algorithm based on spatial transformer network is introduced, which can remove some confounding factors in the original images. The algorithm can obviously improve the inference performance, which has been proven by comparing some evaluation indicators before and after adoption. Such a database will certainly accelerate the application of affective computing in the educational field.