no code implementations • 29 Apr 2024 • Liyuan Wang, Yan Jin, Zhen Chen, Jinlin Wu, Mengke Li, Yang Lu, Hanzi Wang
The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains.
1 code implementation • 23 Apr 2024 • Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen, Mengke Li, Yang Lu, Hanzi Wang
We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.
no code implementations • 18 Apr 2024 • Conggang Hu, Yang Lu, Hongyang Du, Mi Yang, Bo Ai, Dusit Niyato
This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation.
no code implementations • 18 Apr 2024 • Yang Lu, Yuhang Li, Ruichen Zhang, Wei Chen, Bo Ai, Dusit Niyato
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks.
no code implementations • 9 Mar 2024 • Ming Zheng, Yang Yang, Zhi-Hang Zhao, Shan-Chao Gan, Yang Chen, Si-Kai Ni, Yang Lu
In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise.
no code implementations • 5 Feb 2024 • Xiaoheng Jiang, Feng Yan, Yang Lu, Ke Wang, Shuai Guo, Tianzhu Zhang, Yanwei Pang, Jianwei Niu, Mingliang Xu
To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network.
no code implementations • 21 Jan 2024 • Hongjian Gao, Yang Lu, Shaoshi Yang, Jingsheng Tan, Longlong Nie, Xinyi Qu
Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications.
no code implementations • 4 Jan 2024 • Yukang Zhang, Yang Lu, Yan Yan, Hanzi Wang, Xuelong Li
Specifically, we propose a novel Frequency Domain Nuances Mining (FDNM) method to explore the cross-modality frequency domain information, which mainly includes an amplitude guided phase (AGP) module and an amplitude nuances mining (ANM) module.
no code implementations • 21 Dec 2023 • Yang Lu, Jordan Yu, Shou-Hsuan Stephen Huang
This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities.
1 code implementation • 20 Dec 2023 • Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang
The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information.
no code implementations • 20 Dec 2023 • Haohan Wang, Wei Feng, Yang Lu, Yaoyu Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junjie Shen, Zhangang Lin, Lixing Bo, Jingping Shao
Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage.
1 code implementation • 14 Dec 2023 • Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yuan Xie, Yanyun Qu
For server-side learning, in order to mitigate the heterogeneity and class-distribution imbalance, we generate federated features to retrain the server model.
no code implementations • 30 Nov 2023 • Lingzhi Gao, Zexi Li, Yang Lu, Chao Wu
A typical way of pFL focuses on label distribution skew, and they adopt a decoupling scheme where the model is split into a common feature extractor and two prediction heads (generic and personalized).
no code implementations • 7 Nov 2023 • Yuhang Li, Yang Lu, Bo Ai, Octavia A. Dobre, Zhiguo Ding, Dusit Niyato
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
no code implementations • 22 Sep 2023 • Xiaoheng Jiang, Shilong Tian, Zhiwen Zhu, Yang Lu, Hao liu, Li Chen, Shupan Li, Mingliang Xu
In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage.
no code implementations • 22 Sep 2023 • Xiaoheng Jiang, Kaiyi Guo, Yang Lu, Feng Yan, Hao liu, Jiale Cao, Mingliang Xu, DaCheng Tao
To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer.
no code implementations • 22 Sep 2023 • Feng Yan, Xiaoheng Jiang, Yang Lu, Lisha Cui, Shupan Li, Jiale Cao, Mingliang Xu, DaCheng Tao
To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure.
1 code implementation • 12 Jun 2023 • Mengke Li, Zhikai Hu, Yang Lu, Weichao Lan, Yiu-ming Cheung, Hui Huang
To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T).
1 code implementation • CVPR 2022 • Mengke Li, Yiu-ming Cheung, Yang Lu
It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems.
Ranked #13 on Long-tail Learning on CIFAR-10-LT (ρ=100)
1 code implementation • 18 May 2023 • Mengke Li, Yiu-ming Cheung, Yang Lu, Zhikai Hu, Weichao Lan, Hui Huang
Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead.
1 code implementation • 14 Apr 2023 • Xinwen Fan, Yukang Zhang, Yang Lu, Hanzi Wang
Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis.
1 code implementation • CVPR 2023 • Yan Jin, Mengke Li, Yang Lu, Yiu-ming Cheung, Hanzi Wang
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
1 code implementation • 27 Mar 2023 • Yang Lu, Pinxin Qian, Gang Huang, Hanzi Wang
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner.
no code implementations • 4 Mar 2023 • Xinyi Shang, Gang Huang, Yang Lu, Jian Lou, Bo Han, Yiu-ming Cheung, Hanzi Wang
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
1 code implementation • 24 Feb 2023 • Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Lingpeng Kong
RSA links query protein sequences to a set of sequences with similar structures or properties in the database and combines these sequences for downstream prediction.
1 code implementation • 6 Oct 2022 • Ping Xue, Yang Lu, Jingfei Chang, Xing Wei, Zhen Wei
In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR$^2$Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability; 2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification.
no code implementations • 1 Oct 2022 • Yang Lu, Zhengxin Yu, Neeraj Suri
Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem.
no code implementations • 22 Sep 2022 • Lulu Pan, Haibin Shao, Yang Lu, Mehran Mesbahi, Dewei Li, Yugeng Xi
We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state.
no code implementations • 25 Aug 2022 • Ben Duan, Yutian Li, Dawei Lu, Yang Lu, Ran Zhang
The new framework can be easily adopted to local volume and stochastic volume models for the option pricing problem, which will point out a new possible direction for this central problem in quantitative finance.
1 code implementation • ICCV 2023 • Yang Lu, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang
In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution.
no code implementations • 10 Jun 2022 • Ashkan Behzadian, Tanner Wambui Muturi, Tianjie Zhang, Hongmin Kim, Amanda Mullins, Yang Lu, Neema Jasika Owor, Yaw Adu-Gyamfi, William Buttlar, Majidifard Hamed, Armstrong Aboah, David Mensching, Spragg Robert, Matthew Corrigan, Jack Youtchef, Dave Eshan
The Data Science for Pavement Challenge (DSPC) seeks to accelerate the research and development of automated vision systems for pavement condition monitoring and evaluation by providing a platform with benchmarked datasets and codes for teams to innovate and develop machine learning algorithms that are practice-ready for use by industry.
1 code implementation • 30 Apr 2022 • Xinyi Shang, Yang Lu, Yiu-ming Cheung, Hanzi Wang
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data.
2 code implementations • 28 Apr 2022 • Xinyi Shang, Yang Lu, Gang Huang, Hanzi Wang
Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data.
no code implementations • 17 Feb 2022 • Yang Lu, Qifan Chen, Simon K. Poon
The performance of existing process discovery algorithms can be affected when there are missing activity labels in event logs.
no code implementations • 31 Aug 2021 • Jingfei Chang, Yang Lu, Ping Xue, Yiqun Xu, Zhen Wei
We propose a novel adversarial iterative pruning method (AIP) for CNNs based on knowledge transfer.
1 code implementation • 24 Jun 2021 • Yang Lu, Qifan Chen, Simon Poon
Process mining is a relatively new subject which builds a bridge between process modelling and data mining.
no code implementations • 30 Mar 2021 • Qifan Chen, Yang Lu, Charmaine S. Tam, Simon K. Poon
Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours.
no code implementations • 19 Mar 2021 • Qifan Chen, Yang Lu, Simon Poon
The key to process mining is to discovery understandable process models.
no code implementations • 19 Mar 2021 • Yang Lu, Qifan Chen, Simon Poon
In this paper, we propose a method which takes both event logs and process models as input and generates a choice sequence for each exclusive choice in the process model.
1 code implementation • 3 Mar 2021 • Ping Xue, Yang Lu, Jingfei Chang, Xing Wei, Zhen Wei
In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i. e., 1-bit representation).
1 code implementation • 16 Jan 2021 • Jingfei Chang, Yang Lu, Ping Xue, Yiqun Xu, Zhen Wei
While the accuracy loss after pruning based on the structure sensitivity is relatively slight, the process is time-consuming and the algorithm complexity is notable.
no code implementations • 15 Nov 2020 • Dhruv Vashisht, Harshit Rampal, Haiguang Liao, Yang Lu, Devika Shanbhag, Elias Fallon, Levent Burak Kara
Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing.
no code implementations • 3 Oct 2020 • Jingfei Chang, Yang Lu, Ping Xue, Xing Wei, Zhen Wei
For ResNet with bottlenecks, we use the pruning method with traditional CNN to trim the 3x3 convolutional layer in the middle of the blocks.
1 code implementation • 3 Feb 2020 • Yang Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier.
no code implementations • 24 May 2019 • Yang Lu, Xiaohui Liang, Frederick W. B. Li
In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks.
no code implementations • 18 Apr 2019 • Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu
ProductNet is a collection of high-quality product datasets for better product understanding.
no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
no code implementations • 29 Jan 2019 • Yang Lu, Yiu-ming Cheung, Yuan Yan Tang
To the best of our knowledge, there is no any measurement about the extent of influence of class imbalance on the classification performance of imbalanced data.
no code implementations • 31 Dec 2018 • Caleb Tung, Matthew R. Kelleher, Ryan J. Schlueter, Binhan Xu, Yung-Hsiang Lu, George K. Thiruvathukal, Yen-Kuang Chen, Yang Lu
However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e. g. ambient lighting) changes.
no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
no code implementations • CVPR 2018 • Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu
Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image.
no code implementations • 29 Sep 2016 • Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu
Specifically, within each iteration of the cooperative learning algorithm, the generator model generates initial synthesized examples to initialize a finite-step MCMC that samples and trains the energy-based descriptor model.
no code implementations • 28 Jun 2016 • Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu
This paper proposes an alternating back-propagation algorithm for learning the generator network model.
no code implementations • 10 Feb 2016 • Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu
If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then we obtain a generative ConvNet model that is unique among energy-based models: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process.
no code implementations • 28 Sep 2015 • Yang Lu, Song-Chun Zhu, Ying Nian Wu
We explain that each learned model corresponds to a new CNN unit at a layer above the layer of filters employed by the model.
1 code implementation • CVPR 2014 • Tianfu Wu, Yang Lu, Song-Chun Zhu
In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network.
no code implementations • 19 Dec 2014 • Jifeng Dai, Yang Lu, Ying-Nian Wu
(2) We propose a generative gradient for pre-training CNNs by a non-parametric importance sampling scheme, which is fundamentally different from the commonly used discriminative gradient, and yet has the same computational architecture and cost as the latter.