Xiaoyi Wang

Zhen Li, Sunzeng Cai, Xiaoyi Wang et al.

Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city. The challenge is how to model long-term temporal dependencies in an efficient manner. Some recent works employ Recurrent Neural Networks (RNN) to obtain good performance, which, however, requires a large amount of training data. In this paper, we proposed a novel tracking method that integrates the auto-tuning Kalman method for prediction and the Gated Recurrent Unit (GRU) and achieves a near-optimum with a small amount of training data. Experimental results show that our new algorithm can achieve competitive performance on the challenging MOT benchmark, and faster and more robust than the state-of-the-art RNN-based online MOT algorithms.

Xiaoyi Wang, Alexander Eiselmayer, Wendy E. Mackay et al.

A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A prior power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to calculate the statistical power of a given experiment design. We created Argus, a tool that supports interactive exploration of statistical power: Researchers specify experiment design scenarios with varying confounds and effect sizes. Argus then simulates data and visualizes statistical power across these scenarios, which lets researchers interactively weigh various trade-offs and make informed decisions about sample size. We describe the design and implementation of Argus, a usage scenario designing a visualization experiment, and a think-aloud study.

Dingcheng Yang, Wenjian Yu, Ao Zhou et al.

In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantization and an optimization process to train a clustering-friendly DNN. Experiments showed that the DP-Net allows larger compression than the state-of-the-art counterparts while preserving accuracy. The largest 77X compression ratio on Wide ResNet is achieved by combining DP-Net with other compression techniques. Furthermore, the DP-Net is extended for compressing a robust DNN model with negligible accuracy loss. At last, a custom accelerator is designed on FPGA to speed up the inference computation with DP-Net.