Xiaoyu Ren

Xiaoyu Ren, Zhongying Deng, Jin Ye et al.

Fully convolutional network (FCN) is a seminal work for semantic segmentation. However, due to its limited receptive field, FCN cannot effectively capture global context information which is vital for semantic segmentation. As a result, it is beaten by state-of-the-art methods that leverage different filter sizes for larger receptive fields. However, such a strategy usually introduces more parameters and increases the computational cost. In this paper, we propose a novel global receptive convolution (GRC) to effectively increase the receptive field of FCN for context information extraction, which results in an improved FCN termed FCN+. The GRC provides the global receptive field for convolution without introducing any extra learnable parameters. The motivation of GRC is that different channels of a convolutional filter can have different grid sampling locations across the whole input feature map. Specifically, the GRC first divides the channels of the filter into two groups. The grid sampling locations of the first group are shifted to different spatial coordinates across the whole feature map, according to their channel indexes. This can help the convolutional filter capture the global context information. The grid sampling location of the second group remains unchanged to keep the original location information. By convolving using these two groups, the GRC can integrate the global context into the original location information of each pixel for better dense prediction results. With the GRC built in, FCN+ can achieve comparable performance to state-of-the-art methods for semantic segmentation tasks, as verified on PASCAL VOC 2012, Cityscapes, and ADE20K. Our code will be released at https://github.com/Zhongying-Deng/FCN_Plus.

Hui Li, Xiaoyu Ren, Hongjiu Yu et al.

Facial attractiveness prediction (FAP) has long been an important computer vision task, which could be widely applied in live streaming for facial retouching, content recommendation, etc. However, previous FAP datasets are either small, closed-source, or lack diversity. Moreover, the corresponding FAP models exhibit limited generalization and adaptation ability. To overcome these limitations, in this paper we present LiveBeauty, the first large-scale live-specific FAP dataset, in a more challenging application scenario, i.e., live streaming. 10,000 face images are collected from a live streaming platform directly, with 200,000 corresponding attractiveness annotations obtained from a well-devised subjective experiment, making LiveBeauty the largest open-access FAP dataset in the challenging live scenario. Furthermore, a multi-modal FAP method is proposed to measure the facial attractiveness in live streaming. Specifically, we first extract holistic facial prior knowledge and multi-modal aesthetic semantic features via a Personalized Attractiveness Prior Module (PAPM) and a Multi-modal Attractiveness Encoder Module (MAEM), respectively, then integrate the extracted features through a Cross-Modal Fusion Module (CMFM). Extensive experiments conducted on both LiveBeauty and other open-source FAP datasets demonstrate that our proposed method achieves state-of-the-art performance. Dataset will be available soon.