Search Results for author: Lin Wu

Found 39 papers, 6 papers with code

DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction

no code implementations7 Aug 2023 Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu

Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making.

Decision Making Time Series

Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

1 code implementation IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023 Xu Yuan, Zheng Zhang, Xunguang Wang, Lin Wu

Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes.

Adversarial Attack Adversarial Defense +3

Research on Multilingual News Clustering Based on Cross-Language Word Embeddings

no code implementations30 May 2023 Lin Wu, Rui Li, Wong-Hing Lam

(2) We use the LDA topic model to represent news as a combina-tion of cross-lingual vectors for headlines and topic probability distributions for con-tent, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation.

Clustering Knowledge Distillation +2

The News Delivery Channel Recommendation Based on Granular Neural Network

no code implementations30 May 2023 Lin Wu, Rui Li, Jiaxuan Liu, Wong-Hing Lam

As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches.

Collaborative Filtering Machine Translation +4

LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers

no code implementations4 Feb 2023 Feng Xue, Yu Li, Deyin Liu, Yincen Xie, Lin Wu, Richang Hong

However, generalizing these methods to unseen speakers incurs catastrophic performance degradation due to the limited number of speakers in training bank and the evident visual variations caused by the shape/color of lips for different speakers.


Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification

no code implementations9 Jul 2022 Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, ZongYuan Ge, Farid Boussaid, Mohammed Bennamoun, Jialie Shen

In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.

Unsupervised Person Re-Identification

Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification

no code implementations9 Jul 2022 Lin Wu, Lingqiao Liu, Yang Wang, Zheng Zhang, Farid Boussaid, Mohammed Bennamoun

It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.

Person Re-Identification Super-Resolution

Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense

no code implementations9 Jul 2022 Deyin Liu, Lin Wu, Haifeng Zhao, Farid Boussaid, Mohammed Bennamoun, Xianghua Xie

Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.

Adversarial Defense

A coarse-to-fine approach for dynamic-to-static image translation

1 code implementation Pattern Recognition 2021 Teng Wang, Lin Wu, Changyin Sun

Using the coarse predicted image, we explicitly infer a more accurate dynamic mask to identify both dynamic objects and their shadows, so that the task could be effectively converted to an image inpainting problem.

Image Inpainting Image-to-Image Translation +2

Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space

no code implementations17 May 2021 Lin Wu, Teng Wang, Changyin Sun

In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments.

Semantic Segmentation Visual Place Recognition

Symmetry-enforced Band Nodes in 230 Space Groups

no code implementations18 Feb 2021 Lin Wu, Feng Tang, Xiangang Wan

Crystallographic symmetries enforcing band touchings (BTs) in the Brillouin zone (BZ) have been utilized to classify and predict the topological semimetals.

Materials Science

Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation

no code implementations3 Feb 2020 Siqi Yang, Lin Wu, Arnold Wiliem, Brian C. Lovell

To achieve gradient alignment, we propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.

Image Classification object-detection +2

Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks

no code implementations14 Nov 2019 Deyin Liu, Lin Wu, Xue Li

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

1 code implementation24 Jun 2019 Meng Li, Lin Wu, Arnold Wiliem, Kun Zhao, Teng Zhang, Brian C. Lovell

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.

General Classification Multiple Instance Learning

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

no code implementations24 Jun 2019 Sam Maksoud, Arnold Wiliem, Kun Zhao, Teng Zhang, Lin Wu, Brian C. Lovell

This is because the system can ignore the attention mechanism by assigning equal weights for all members.


Cross-Entropy Adversarial View Adaptation for Person Re-identification

no code implementations3 Apr 2019 Lin Wu, Richang Hong, Yang Wang, Meng Wang

The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective.

Person Re-Identification

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

no code implementations29 Mar 2019 Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, Ling Shao

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.

Time Series Time Series Analysis +1

3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification

no code implementations26 Dec 2018 Lin Wu, Yang Wang, Ling Shao, Meng Wang

In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent.

Video-Based Person Re-Identification

Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification

no code implementations3 Aug 2018 Lin Wu, Yang Wang, Junbin Gao, Xue Li

Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security.

Metric Learning Video-Based Person Re-Identification

Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

1 code implementation30 Apr 2018 Lin Wu, Yang Wang, Junbin Gao, DaCheng Tao

Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring.

Foveation Person Re-Identification +1

Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

no code implementations30 Apr 2018 Lin Wu, Yang Wang, Ling Shao

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss.

Cross-Modal Retrieval Retrieval +1

Crossing Generative Adversarial Networks for Cross-View Person Re-identification

no code implementations4 Jan 2018 Chengyuan Zhang, Lin Wu, Yang Wang

Given a pair of person images, the proposed model consists of the variational auto-encoder to encode the pair into respective latent variables, a proposed cross-view alignment to reduce the view disparity, and an adversarial layer to seek the joint distribution of latent representations.

Cross-Modal Person Re-Identification

When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity

no code implementations14 Oct 2017 Tong Chen, Lin Wu, Yang Wang, Jun Zhang, Hongxu Chen, Xue Li

Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.

Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition

no code implementations18 Sep 2017 Lin Wu, Yang Wang

Given an image, two different Convolutional Neural Networks (CNNs) are constructed, where the outputs of two CNNs are correlated through bilinear pooling to simultaneously focus on discriminative regions and extract relevant features.

Deep Attention Fine-Grained Image Classification +2

Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization

no code implementations5 Sep 2017 Yang Wang, Lin Wu

However, as we observed, such classical paradigm still suffers from (1) overlooking the flexible local manifold structure, caused by (2) enforcing the low-rank data correlation agreement among all views; worse still, (3) LRR is not intuitively flexible to capture the latent data clustering structures.


Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering

no code implementations4 Aug 2017 Yang Wang, Lin Wu

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts.


What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

no code implementations21 Jul 2017 Lin Wu, Yang Wang, Xue Li, Junbin Gao

To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way.

Person Re-Identification

Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

no code implementations10 Jun 2017 Lin Wu, Yang Wang, Junbin Gao, Xue Li

To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding.

Metric Learning Person Re-Identification

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

no code implementations20 Apr 2017 Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, Yang Wang

The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.

Deep Attention

Finding Modes by Probabilistic Hypergraphs Shifting

no code implementations12 Apr 2017 Yang Wang, Lin Wu

Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i. e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes.

Clustering Graph Matching

Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification

no code implementations14 Feb 2017 Lin Wu, Yang Wang

Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person captured by disjoint camera views.

Person Re-Identification

Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval

no code implementations18 Jan 2017 Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo.

Collaborative Filtering Retrieval

Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions

no code implementations17 Nov 2016 Lin Wu, Yang Wang

To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data.

graph construction

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

no code implementations19 Aug 2016 Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.


Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification

no code implementations6 Jun 2016 Lin Wu, Chunhua Shen, Anton Van Den Hengel

Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters.

Person Re-Identification

Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach

no code implementations6 Jun 2016 Lin Wu, Chunhua Shen, Anton Van Den Hengel

In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification.

Metric Learning Time Series +2

PersonNet: Person Re-identification with Deep Convolutional Neural Networks

1 code implementation27 Jan 2016 Lin Wu, Chunhua Shen, Anton Van Den Hengel

In this paper, we propose a deep end-to-end neu- ral network to simultaneously learn high-level features and a corresponding similarity metric for person re-identification.

Person Re-Identification

Structured learning of metric ensembles with application to person re-identification

no code implementations27 Nov 2015 Sakrapee Paisitkriangkrai, Lin Wu, Chunhua Shen, Anton Van Den Hengel

However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.

Metric Learning Person Re-Identification

Cannot find the paper you are looking for? You can Submit a new open access paper.