Author name cluster

Yue Guo

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4 papers

2 author rows

IROS Conference 2022 Conference Paper

3D Single-Object Tracking with Spatial-Temporal Data Association

Yongchang Zhang
Hanbing Niu
Yue Guo
Wenhao He

This paper proposes a novel 3D single-object tracker to more stably, accurately, and faster track objects, even if they are temporarily missed. Our idea is to utilize spatial-temporal data association to achieve object tracking robustly, and it consists of two main parts. We firstly employ a temporal motion model cross frames to estimate the object's temporal information and update the region of interest(ROI). The advanced detector only focuses on ROI rather than the whole scene to generate the spatial position. Second, we introduce a new pairwise evaluation system to exploit spatial-temporal data association in point clouds. The proposed evaluation system considers detection confidence, orientation offset, and objects distance to more stably achieve object matching. Then, we update the predicted state based on the pairwise spatial-temporal data. Finally, we utilize the previous trajectory to enhance the accuracy of static tracking in the refinement scheme. Experiments on the KITTI and nuScenes tracking datasets demonstrate that our method outperforms other state-of-the-art methods by a large margin (a 10% improvement and 280 FPS on a single NVIDIA 1080Ti GPU). Compared with multi-object tracking, our tracker also has superiority.

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AAAI Conference 2021 Conference Paper

Automated Lay Language Summarization of Biomedical Scientific Reviews

Yue Guo
Wei Qiu
Yizhong Wang
Trevor Cohen

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50. 24 and Flesch-Kincaid readability score of 13. 30). We also discuss the limitations of the current effort, providing insights and directions for future work.

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AAAI Conference 2021 Conference Paper

Learning Modulated Loss for Rotated Object Detection

Wen Qian
Xue Yang
Silong Peng
Junchi Yan
Yue Guo

Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) or eight parameters (coordinates of four vertices) to describe the rotated bounding box and `1 loss as the loss function. In this paper, we argue that the aforementioned integration can cause training instability and performance degeneration. The main reason is the discontinuity of loss which is caused by the contradiction between the definition of the rotated bounding box and the loss function. We refer to the above issues as rotation sensitivity error (RSE) and propose a modulated rotation loss to dismiss the discontinuity of loss. The modulated rotation loss can achieve consistent improvement on the five parameter methods and the eight parameter methods. Experimental results using one stage and two stages detectors demonstrate the effectiveness of our loss. The integrated network achieves competitive performances on several benchmarks including DOTA and UCAS AOD. The code is available at https: //github. com/ yangxue0827/RotationDetection.

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SODA Conference 2018 Conference Paper

Cache-Oblivious and Data-Oblivious Sorting and Applications

T. -H. Hubert Chan
Yue Guo
Wei-Kai Lin
Elaine Shi

Although external-memory sorting has been a classical algorithms abstraction and has been heavily studied in the literature, perhaps somewhat surprisingly, when data-obliviousness is a requirement, even very rudimentary questions remain open. Prior to our work, it is not even known how to construct a comparison-based, external-memory oblivious sorting algorithm that is optimal in IO-cost. We make a significant step forward in our understanding of external-memory, oblivious sorting algorithms. Not only do we construct a comparison-based, external-memory oblivious sorting algorithm that is optimal in IO-cost, our algorithm is also cache-agnostic in that the algorithm need not know the storage hierarchy's internal parameters such as the cache and cache-line sizes. Our result immediately implies a cache-agnostic ORAM construction whose asymptotic IO-cost matches the best known cache-aware scheme. Last but not the least, we propose and adopt a new and stronger security notion for external-memory, oblivious algorithms and argue that this new notion is desirable for resisting possible cache-timing attacks. Thus our work also lays a foundation for the study of oblivious algorithms in the cache-agnostic model.

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