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Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.


Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss.


In this paper, we propose a novel virtual reality image quality assessment (VR IQA) with adversarial learning for omnidirectional images. To take into account the characteristics of the omnidirectional image, we devise deep networks including novel quality score predictor and human perception guider. The proposed quality score predictor automatically predicts the quality score of distorted image using the latent spatial and position feature.


In this paper, we propose variational fusion of time-of-flight (TOF) and stereo data using edge selective joint filtering (ESJF). We utilize ESJF to up-sample low-resolution (LR) depth captured by TOF camera and produce high-resolution (HR)depth maps with accurate edge information. First, we measure confidence of two sensor with different reliability to fuse them. Then, we up-sample TOF depth map using ESJF to generate discontinuity maps and protect edges in depth. Finally, we perform variational fusion of TOF and stereo depth data based on total variation (TV) guided by discontinuity maps.