In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level encoder features are repeatedly used at multiple scales. Second, the contextual information of the low-level encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature. In this study, we propose a novel spatial-channel attention gate that addresses the limitations of plain skip connections. This can be easily integrated into an encoder-decoder network to effectively improve the performance of the image segmentation task. Comprehensive results reveal that our spatial-channel attention gate remarkably enhances the segmentation capability of the U-Net architecture with a minimal computational overhead added. The experimental results show that our proposed method outperforms the conventional deep networks in term of Dice score, which achieves 71.72%.

Emotion recognition is one of the hottest fields in affective computing research. Recognizing emotions is an important task for facilitating communication between machines and humans. However, it is a very challenging task based on a lack of ethnically diverse databases. In particular, emotional expressions tend to be very dissimilar between Western and Eastern people. Therefore, diverse emotion databases are required for studying emotional expression. However, majority of the well-known emotion databases focus on Western people, which exhibit different characteristics compared to Eastern people. In this study, we constructed a novel emotion dataset containing more than 1200 video clips collected from Korean movies, called Korean Video Dataset for Emotion Recognition in the Wild (KVDERW). Which are similar to real-world conditions, with the goal of studying the emotions of Eastern people, particularly Korean people. Additionally, we developed a semi-automatic video emotion labelling tool that could be used to generate video clips and annotate the emotions in clips.

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