Browsing by Author "Zhang, Xiaolong"

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    CLA-GAN: A Context and Lightness Aware Generative Adversarial Network for Shadow Removal
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Ling; Long, Chengjiang; Yan, Qingan; Zhang, Xiaolong; Xiao, Chunxia; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    In this paper, we propose a novel context and lightness aware Generative Adversarial Network (CLA-GAN) framework for shadow removal, which refines a coarse result to a final shadow removal result in a coarse-to-fine fashion. At the refinement stage, we first obtain a lightness map using an encoder-decoder structure. With the lightness map and the coarse result as the inputs, the following encoder-decoder tries to refine the final result. Specifically, different from current methods restricted pixel-based features from shadow images, we embed a context-aware module into the refinement stage, which exploits patch-based features. The embedded module transfers features from non-shadow regions to shadow regions to ensure the consistency in appearance in the recovered shadow-free images. Since we consider pathces, the module can additionally enhance the spatial association and continuity around neighboring pixels. To make the model pay more attention to shadow regions during training, we use dynamic weights in the loss function. Moreover, we augment the inputs of the discriminator by rotating images in different degrees and use rotation adversarial loss during training, which can make the discriminator more stable and robust. Extensive experiments demonstrate the validity of the components in our CLA-GAN framework. Quantitative evaluation on different shadow datasets clearly shows the advantages of our CLA-GAN over the state-of-the-art methods.
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    Tac-Anticipator: Visual Analytics of Anticipation Behaviors in Table Tennis Matches
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Wang, Jiachen; Wu, Yihong; Zhang, Xiaolong; Zeng, Yixin; Zhou, Zheng; Zhang, Hui; Xie, Xiao; Wu, Yingcai; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in-lab behaviors, failed to capture the characteristics of in-situ anticipation behaviors in real matches. This research proposes a data-driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac-Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.