no code implementations • 5 Mar 2024 • Wei Bao, Mi Zhang, Tao Zhang, Chengfu Huo
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions. Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process, the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model. 2)intention transfer (IT):previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer. However, the current intention extracted from prefix may be contrary to the historical preferences.
no code implementations • 25 Aug 2023 • Jiaming Shen, Kun Hu, Wei Bao, Chang Wen Chen, Zhiyong Wang
The 2D animation workflow is typically initiated with the creation of keyframes using sketch-based drawing.
no code implementations • 16 Apr 2023 • Yu Zhang, Huaming Chen, Wei Bao, Zhongzheng Lai, Zao Zhang, Dong Yuan
Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.
no code implementations • 20 Mar 2023 • Nan Yang, Xuanyu Chen, Charles Z. Liu, Dong Yuan, Wei Bao, Lizhen Cui
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of expertise.
no code implementations • 23 Feb 2023 • Nan Yang, Dong Yuan, Charles Z Liu, Yongkun Deng, Wei Bao
Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of expertise.
no code implementations • 17 Feb 2023 • Nan Yang, Laicheng Zhong, Fan Huang, Dong Yuan, Wei Bao
Random Padding is parameter-free, simple to construct, and compatible with the majority of CNN-based recognition models.
no code implementations • ICCV 2023 • Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu
The issues are, that we do not understand why label dependence is helpful in the problem, and how to learn and utilize label dependence only using training data with noisy multiple labels.
no code implementations • 23 Dec 2022 • Tung-Anh Nguyen, Jiayu He, Long Tan Le, Wei Bao, Nguyen H. Tran
To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.
no code implementations • 26 Oct 2022 • Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan, Albert Y. Zomaya
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration.
no code implementations • NeurIPS 2021 • Xiuwen Gong, Dong Yuan, Wei Bao
To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly.
no code implementations • 31 Aug 2021 • Xiuwen Gong, Dong Yuan, Wei Bao
The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process.
no code implementations • 15 Mar 2021 • Wei Bao, Meiyu Huang, Yaqin Zhang, Yao Xu, Xuejiao Liu, Xueshuang Xiang
In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
1 code implementation • 15 Mar 2021 • Meiyu Huang, Yao Xu, Lixin Qian, Weili Shi, Yaqin Zhang, Wei Bao, Nan Wang, Xuejiao Liu, Xueshuang Xiang
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
no code implementations • 24 Jan 2021 • Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, Wenjun Wu
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
2 code implementations • 10 Dec 2020 • Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, Bing B. Zhou, Albert Y. Zomaya
In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
no code implementations • SEMEVAL 2020 • Wei Bao, Hongshu Che, Jiandong Zhang
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years.
no code implementations • SEMEVAL 2020 • Wei Bao, Weilong Chen, Wei Bai, Yan Zhuang, Mingyuan Cheng, Xiangyu Ma
Mixing languages are widely used in social media, especially in multilingual societies like India.
no code implementations • 18 Sep 2020 • Zhengjie Yang, Wei Bao, Dong Yuan, Nguyen H. Tran, Albert Y. Zomaya
It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.
no code implementations • 12 Jun 2020 • Xiuwen Gong, Jiahui Yang, Dong Yuan, Wei Bao
Specifically, in order to learn the new $k$NN-based metric, we first project instances in the training dataset into the label space, which make it possible for the comparisons of instances and labels in the same dimension.
no code implementations • 3 May 2020 • Wei Bao, Hongshu Che, Jiandong Zhang
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years.
4 code implementations • 29 Oct 2019 • Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen, Choong Seon Hong, Wei Bao, Albert Y. Zomaya, Vincent Gramoli
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.