Browsing by Author "Liu, Yu"

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    DARC: A Visual Analytics System for Multivariate Applicant Data Aggregation, Reasoning and Comparison
    (The Eurographics Association, 2022) Hou, Yihan; Liu, Yu; Wang, He; Zhang, Zhichao; Li, Yue; Liang, Hai-Ning; Yu, Lingyun; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
    People often make decisions based on their comprehensive understanding of various materials, judgement of reasons, and comparison among choices. For instance, when hiring committees review multivariate applicant data, they need to consider and compare different aspects of the applicants' materials. However, the amount and complexity of multivariate data increase the difficulty to analyze the data, extract the most salient information, and then rapidly form opinions based on the extracted information. Thus, a fast and comprehensive understanding of multivariate data sets is a pressing need in many fields, such as business and education. In this work, we had in-depth interviews with stakeholders and characterized user requirements involved in data-driven decision making in reviewing school applications. Based on these requirements, we propose DARC, a visual analytics system for facilitating decision making on multivariate applicant data. Through the system, users are supported to gain insights of the multivariate data, picture an overview of all data cases, and retrieve original data in a quick and intuitive manner. The effectiveness of DARC is validated through observational user evaluations and interviews.
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    Semi-Supervised 3D Shape Recognition via Multimodal Deep Co-training
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Song, Mofei; Liu, Yu; Liu, Xiao Fan; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    3D shape recognition has been actively investigated in the field of computer graphics. With the rapid development of deep learning, various deep models have been introduced and achieved remarkable results. Most 3D shape recognition methods are supervised and learn only from the large amount of labeled shapes. However, it is expensive and time consuming to obtain such a large training set. In contrast to these methods, this paper studies a semi-supervised learning framework to train a deep model for 3D shape recognition by using both labeled and unlabeled shapes. Inspired by the co-training algorithm, our method iterates between model training and pseudo-label generation phases. In the model training phase, we train two deep networks based on the point cloud and multi-view representation simultaneously. In the pseudo-label generation phase, we generate the pseudo-labels of the unlabeled shapes using the joint prediction of two networks, which augments the labeled set for the next iteration. To extract more reliable consensus information from multiple representations, we propose an uncertainty-aware consistency loss function to combine the two networks into a multimodal network. This not only encourages the two networks to give similar predictions on the unlabeled set, but also eliminates the negative influence of the large performance gap between the two networks. Experiments on the benchmark ModelNet40 demonstrate that, with only 10% labeled training data, our approach achieves competitive performance to the results reported by supervised methods.