Depression can be reflected by long-term human spatio-temporal facial behaviours. While human face videos recorded in real-world usually have long and variable lengths, existing video-based depression assessment approaches frequently re-sample/down-sample such videos to short and equal-length videos, or split each video into several equal-length segments, where segment-level spatio-temporal facial behaviours are suppressed as a vector-style representations for RNN-based long-term (video-level) modelling. Both strategies lead to crucial information loss and distortion. In this paper, we propose a novel graph-style data structure called Matrixial Graph and an effective Matrixial Graph Neural Network (MGNN) for face video-based depression assessment, which can directly and end-to-end model long-term depression-specific spatio-temporal facial cues from variable-length videos without resampling/splitting videos or suppressing video segments to vectors. Importantly, the nodes in our matrixial graph are capable of including matrices of different shapes, and thus nodes of a matrix graph can directly represent all frame-level 2D facial feature maps (or images themselves) of an entire video regardless of its length. Then, our MGNN is the first GNN that can jointly process matrixial graphs containing varying numbers of nodes, which further learns matrix-style edge features, thereby facilitating to explicit model video-level multi-scale spatio-temporal facial behaviours among matrixial graph nodes for depression assessment. Experiments show that the explicit spatio-temporal modeling on 2D facial feature maps, facilitated by our matrixial graph/MGNN, provided significant benefits, leading our approach to achieve new state-of-the-art performances on AVEC2013 and AVEC2014 datasets with large advantages.

Cross-cultural design requires designers to understand other foreign cultures, selecting suitable cultural elements, and finally incorporate them into product design. Traditionally, this process is time-consuming and relies to a significant extent on designers’ cultural awareness and design skills. This article proposes a new tool for designers to select and integrate cultural elements in the cross-cultural design process. The proposed approach utilizes state-of-the-art deep learning techniques, which begins by automatically selecting the most suitable style image from all cultural image candidates. Then, the deep-learning-based style transfer technique is introduced to automatically produce a design image that has the same content as the uploaded design content image, and also has the cultural style of the selected style image. To the best of our knowledge, this is the first work that extends deep learning techniques to facilitate cross-cultural design. The tool received positive feedback in a usability evaluation. The empirical results show that our approach can effectively increase designers’ cultural awareness in respect of four cultural element dimensions (color, material, pattern and form). It is an innovative and efficient tool to help designers with idea generation and fast prototyping, although some participants argued that the tool would only assist designers, rather than replace humans.