Search Results for author: Yixuan Liu

Found 7 papers, 3 papers with code

Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking

1 code implementation ACL 2022 Jinyu Guo, Kai Shuang, Jijie Li, Zihan Wang, Yixuan Liu

However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.

Dialogue State Tracking

Extremal Analysis of Flooding Risk and Management

no code implementations1 Dec 2021 Chengxiu Ling, Jiayi Li, Yixuan Liu, Zhiyan Cai

Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency.

Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning

1 code implementation30 Nov 2021 Yixuan Liu, Chrysafis Vogiatzis, Ruriko Yoshida, Erich Morman

Uncrewed autonomous vehicles (UAVs) have made significant contributions to reconnaissance and surveillance missions in past US military campaigns.

Autonomous Vehicles Q-Learning

Delayed Rewards Calibration via Reward Empirical Sufficiency

no code implementations21 Feb 2021 Yixuan Liu, Hu Wang, Xiaowei Wang, Xiaoyue Sun, Liuyue Jiang, Minhui Xue

Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.

CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

no code implementations ECCV 2020 Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Ya-Li Li, Shengjin Wang

This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.

Multi-Object Tracking Person Re-Identification +1

Towards Real-Time Multi-Object Tracking

11 code implementations ECCV 2020 Zhongdao Wang, Liang Zheng, Yixuan Liu, Ya-Li Li, Shengjin Wang

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

Ranked #12 on Multi-Object Tracking on MOT16 (using extra training data)

Multiple Object Tracking Multi-Task Learning +1

Adversarial View-Consistent Learning for Monocular Depth Estimation

no code implementations4 Aug 2019 Yixuan Liu, Yuwang Wang, Shengjin Wang

To this end, we first design a differentiable depth map warping operation, which is end-to-end trainable, and then propose a pose generator to generate novel views for a given image in an adversarial manner.

Monocular Depth Estimation

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