1 code implementation • 22 Apr 2024 • Xiaofei Zhu, Liang Li, Stefan Dietze, Xin Luo
To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation.
no code implementations • 31 Mar 2024 • Chunyang Bi, Xin Luo, Sheng Shen, Mengxi Zhang, Huanjing Yue, Jingyu Yang
In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations based on the learned representations.
1 code implementation • 19 Feb 2024 • Jialiang Wang, Weiling Li, Yurong Zhong, Xin Luo
The performance of an LFA model relies heavily on its training process, which is a non-convex optimization.
no code implementations • 1 Feb 2024 • Li-Jun Zhao, Zhen-Duo Chen, Zi-Chao Zhang, Xin Luo, Xin-Shun Xu
Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes.
no code implementations • 26 Nov 2023 • Yu-Wei Zhan, Fan Liu, Xin Luo, Liqiang Nie, Xin-Shun Xu, Mohan Kankanhalli
To capitalize on these rich Human-Centric Visual Cues, we propose a novel approach named HCVC for HOI detection.
no code implementations • 13 Oct 2023 • Jiale Liu, Yu-Wei Zhan, Chong-Yu Zhang, Xin Luo, Zhen-Duo Chen, Yinwei Wei, Xin-Shun Xu
For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).
no code implementations • 19 Sep 2023 • Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo
Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence.
1 code implementation • ICCV 2023 • Xin Luo, Yunan Zhu, Shunxin Xu, Dong Liu
We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i. e., GAN-based SR), as SR image quality is susceptible to spectral changes.
Image Super-Resolution No-Reference Image Quality Assessment
no code implementations • 19 Jun 2023 • Liping Zhang, Di wu, Xin Luo
Then, based on the idea of stacking ensemble, long short-term memory is employed as an error correction module to forecast the components separately, and the forecast results are treated as new features to be fed into extreme gradient boosting for the second-step forecasting.
no code implementations • 6 Jun 2023 • Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo
An Undirected Weighted Network (UWN) is commonly found in big data-related applications.
no code implementations • 6 Jun 2023 • Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e. g., a protein interaction network from a bioinformatics application.
1 code implementation • 31 May 2023 • Manchang Jin, Gaosheng Liu, Kunshu Hu, Xin Luo, Kun Li, Jingyu Yang
Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures.
no code implementations • 25 Feb 2023 • Ruiyang Xu, Di wu, Xin Luo
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly.
no code implementations • 23 Feb 2023 • ZhiGang Liu, Xin Luo
Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning.
1 code implementation • journal 2023 • ZhiGang Liu, Xin Luo, Zidong Wang, Xiaohui Liu
Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector well-aware of community structure within the target network.
1 code implementation • 22 Dec 2022 • Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie
The booming development and huge market of micro-videos bring new e-commerce channels for merchants.
no code implementations • 20 Dec 2022 • Cheng Liang, Teng Huang, Yi He, Song Deng, Di wu, Xin Luo
The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models.
no code implementations • 15 Dec 2022 • Guodong Chen, Xin Luo, Chuanyin Jiang, Jiu Jimmy Jiao
To solve this issue, a novel surrogate-assisted level-based learning evolutionary search algorithm (SLLES) is proposed for heat extraction optimization of enhanced geothermal system.
no code implementations • 30 Nov 2022 • Ying Wang, Ye Yuan, Xin Luo
Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.
1 code implementation • 27 Nov 2022 • Wei Chen, Chen Li, Dan Chen, Xin Luo
Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.
no code implementations • 10 Nov 2022 • Zhidong Tang, Zewei Wang, Yumeng Yuan, Chang He, Xin Luo, Ao Guo, Renhe Chen, Yongqi Hu, Longfei Yang, Chengwei Cao, Linlin Liu, Liujiang Yu, Ganbing Shang, Yongfeng Cao, Shoumian Chen, Yuhang Zhao, Shaojian Hu, Xufeng Kou
Furthermore, by incorporating the Cryo-CMOS compact model into the process design kit (PDK), the cryogenic 4 Kb SRAM, 5-bit flash ADC and 8-bit current steering DAC are designed, and their performance is readily investigated and optimized on the EDA-compatible platform, hence laying a solid foundation for large-scale cryogenic IC design.
no code implementations • 28 Oct 2022 • Yan Wang, Xin Luo, Zhen-Duo Chen, Peng-Fei Zhang, Meng Liu, Xin-Shun Xu
As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.
no code implementations • 27 Oct 2022 • Jiale Liu, Yu-Wei Zhan, Xin Luo, Zhen-Duo Chen, Yongxin Wang, Xin-Shun Xu
And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky.
no code implementations • 7 Jun 2022 • Guodong Chen, Xin Luo, Jimmy Jiu Jiao, Xiaoming Xue
In this work, a novel and efficient data-driven evolutionary algorithm, called generalized data-driven differential evolutionary algorithm (GDDE), is proposed to reduce the number of simulation runs on well-placement and control optimization problems.
no code implementations • 5 May 2022 • Ye Yuan, Xin Luo
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.
no code implementations • 16 Apr 2022 • Di wu, Yi He, Xin Luo
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system.
no code implementations • 16 Apr 2022 • Di wu, Peng Zhang, Yi He, Xin Luo
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications.
no code implementations • 12 Apr 2022 • Yu-Wei Zhan, Xin Luo, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu
To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches.
no code implementations • 11 Apr 2022 • Yurong Zhong, Xin Luo
An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix.
no code implementations • 2 Apr 2022 • Jia Chen, Di wu, Xin Luo
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications.
no code implementations • 30 Mar 2022 • Ye Yuan, Guangxiao Yuan, Renfang Wang, Xin Luo
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.
no code implementations • 24 Mar 2022 • Zi-Chao Zhang, Zhen-Duo Chen, Yongxin Wang, Xin Luo, Xin-Shun Xu
Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks. However, there are some limitations when applying ViT directly to FGVC. First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects. Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information.
no code implementations • 8 Mar 2022 • ZhiGang Liu, Xin Luo
Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle.
no code implementations • 4 Mar 2022 • Weiling Li, Xin Luo
Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set.
no code implementations • 18 Oct 2021 • Zhi Li, Haoliang Li, Xin Luo, Yongjian Hu, Kwok-Yan Lam, Alex C. Kot
In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.
1 code implementation • 9 Sep 2021 • Xiao-Ming Wu, Xin Luo, Yu-Wei Zhan, Chen-Lu Ding, Zhen-Duo Chen, Xin-Shun Xu
With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic.
no code implementations • 23 Jun 2021 • Xin Luo, Wei Chen, Yusong Tan, Chen Li, Yulin He, Xiaogang Jia
It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data.
no code implementations • IEEE Transactions on Services Computing 2021 • Xin Luo, Yue Zhou, ZhiGang Liu, Lun Hu, Mengchu Zhou
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications.
no code implementations • 14 Jan 2021 • ZhiHao Zhou, Wei Liu, Jiajing He, Lei Chen, Xin Luo, Dongyi Shen, Jianjun Cao, Yaping Dan, Xianfeng Chen, Wenjie Wan
Abbe's resolution limit, one of the best-known physical limitations, poses a great challenge for any wave systems in imaging, wave transport, and dynamics.
Super-Resolution Optics
no code implementations • 16 Sep 2020 • Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu
However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i. e., social images are usually generated periodically or collected in a stream fashion.
no code implementations • 25 Feb 2020 • Huiling Zhu, Xin Luo, Hankz Hankui Zhuo
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications.
no code implementations • ICLR 2019 • Xin Luo, Hankz Hankui Zhuo
Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks.
no code implementations • NAACL 2018 • Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo
The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding.