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Creators/Authors contains: "Wang, Weiyao"

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  1. ; ; ; ; ; ; ; ; (, IEEE International Conference on Intelligent Robots and Systems)
    To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset Omni-Pose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes. 
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    Free, publicly-accessible full text available October 25, 2026
  2. ; ; (, 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE))
    Full Text Available 
  3. ; ; ; (, 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE))
    Full Text Available 
  4. ; ; ; (, The 32’nd Annual Conference on Learning Theory (COLT 2019))
    Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective. For nonconvex objectives, these techniques can also find a first-order stationary point (with small gradient). However, in nonconvex optimization it is often crucial to find a second-order stationary point (with small gradient and almost PSD hessian). In this paper, we show that Stabilized SVRG (a simple variant of SVRG) can find an \eps-second-order stationary point using only O(n^{2/3}/\eps^2+n/\eps^{1.5}) stochastic gradients. To our best knowledge, this is the first second-order guarantee for a simple variant of SVRG. The running time almost matches the known guarantees for finding \eps-first-order stationary points. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; (, ACS Central Science)