In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (an image or video). Due to factors such as illumination, posture, media source, etc., there are huge intra-set variances in a face set, and the importance of different face prototypes varies considerably. How to model the attention mechanism according to the relationship between prototypes or images in a set is the main content of this paper. In this work, we formulate a framework based on graph convolutional network (GCN), which considers face prototypes as nodes to build relations. Specifically, we first present a multi-scale graph module to learn the relationship between prototypes at multiple scales. Moreover, a Contrastive Graph Convolutional (CGC) block is introduced to build attention control model, which focuses on those frames with similar prototypes (contrastive information) between pair of sets instead of simply evaluating the frame quality. The experiments on IJB-A, YouTube Face, and an animal face dataset clearly demonstrate that our proposed MC-GCN outperforms the state-of-the-art methods significantly.

With the development of deep convolutional neural networks, image matting has ushered in a new phase. Regarding the nature of image matting, most researches have focused on solutions for transition regions. However, we argue that many existing approaches are excessively focused on transition-dominant local fields and ignored the inherent coordination between global information and transition optimisation. In this paper, we propose the Tripartite Information Mining and Integration Network (TIMI-Net) to harmonize the coordination between global and local attributes formally. Specifically, we resort to a novel 3-branch encoder to accomplish comprehensive mining of the input information, which can supplement the neglected coordination between global and local fields. In order to achieve effective and complete interaction between such multi-branches information, we develop the Tripartite Information Integration (T I2) Module to transform and integrate the interconnections between the different branches. In addition, we built a large-scale human matting dataset (Human-2K) to advance human image matting, which consists of 2100 high-precision human images (2000 images for training and 100 images for test). Finally, we con-duct extensive experiments to prove the performance of our proposed TIMI-Net, which demonstrates that our method performs favourably against the SOTA approaches on the alphamatting.com (Rank First), Composition-1K (MSE- 0.006, Grad-11.5), Distinctions-646 and our Human-2K. Also, we have developed an online evaluation website to perform natural image matting.