Search Results for author: Jingjing Li

Found 69 papers, 32 papers with code

A Survey of Text Watermarking in the Era of Large Language Models

no code implementations13 Dec 2023 Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li, Xuming Hu, Xi Zhang, Lijie Wen, Irwin King, Hui Xiong, Philip S. Yu

Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.

Dialogue Generation

Order-preserving Consistency Regularization for Domain Adaptation and Generalization

1 code implementation ICCV 2023 Mengmeng Jing, XianTong Zhen, Jingjing Li, Cees Snoek

To alleviate this problem, data augmentation coupled with consistency regularization are commonly adopted to make the model less sensitive to domain-specific attributes.

Data Augmentation Domain Adaptation +1

Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions

1 code implementation28 Aug 2023 Fengling Li, Lei Zhu, Tianshi Wang, Jingjing Li, Zheng Zhang, Heng Tao Shen

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users demanding access to data from various modalities.

Cross-Modal Retrieval Retrieval

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

1 code implementation17 Aug 2023 Haotian Si, Changhua Pei, Zhihan Li, Yadong Zhao, Jingjing Li, Haiming Zhang, Zulong Diao, Jianhui Li, Gaogang Xie, Dan Pei

Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.

Anomaly Detection Multi-Task Learning +3

Zero-Shot Learning by Harnessing Adversarial Samples

1 code implementation1 Aug 2023 Zhi Chen, Pengfei Zhang, Jingjing Li, Sen Wang, Zi Huang

To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS).

Attribute Generalized Zero-Shot Learning +1

VOLTA: Diverse and Controllable Question-Answer Pair Generation with Variational Mutual Information Maximizing Autoencoder

no code implementations3 Jul 2023 Yueen Ma, Dafeng Chi, Jingjing Li, Yuzheng Zhuang, Jianye Hao, Irwin King

Previous question-answer pair generation methods aimed to produce fluent and meaningful question-answer pairs but tend to have poor diversity.

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

1 code implementation12 Apr 2023 Wei Ji, Jingjing Li, Qi Bi, TingWei Liu, Wenbo Li, Li Cheng

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B).

Image Segmentation Segmentation +1

Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment

1 code implementation8 Mar 2023 Jinghan Ru, Jun Tian, Zhekai Du, Chengwei Xiao, Jingjing Li, Heng Tao Shen

To alleviate the negative effects raised by label shift in OSDA, we propose Open-set Moving-threshold Estimation and Gradual Alignment (OMEGA) - a novel architecture that improves existing OSDA methods on class-imbalanced data.

Transfer Learning Unsupervised Domain Adaptation

A Comprehensive Survey on Source-free Domain Adaptation

no code implementations23 Feb 2023 Zhiqi Yu, Jingjing Li, Zhekai Du, Lei Zhu, Heng Tao Shen

Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the source domain.

Source-Free Domain Adaptation Transfer Learning

Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning

no code implementations26 Dec 2022 Jiwei Wei, Yang Yang, Zeyu Ma, Jingjing Li, Xing Xu, Heng Tao Shen

In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.

Zero-Shot Learning

Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks

1 code implementation CVPR 2023 Anqi Zhao, Tong Chu, Yahao Liu, Wen Li, Jingjing Li, Lixin Duan

On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples.

Variational Model Perturbation for Source-Free Domain Adaptation

1 code implementation19 Oct 2022 Mengmeng Jing, XianTong Zhen, Jingjing Li, Cees G. M. Snoek

Our model perturbation provides a new probabilistic way for domain adaptation which enables efficient adaptation to target domains while maximally preserving knowledge in source models.

Bayesian Inference Source-Free Domain Adaptation

How a Brand's Social Activism Impacts Consumers' Brand Evaluations: The Role of Brand Relationship Norms

no code implementations19 Oct 2022 Jingjing Li, Nicole Montgomery, Reza Mousavi

These findings are attributable to consumers' perceptions of whether the brand's response strategy complies with relationship norms during social activism.

Federated Zero-Shot Learning for Visual Recognition

no code implementations5 Sep 2022 Zhi Chen, Yadan Luo, Sen Wang, Jingjing Li, Zi Huang

We identify two key challenges in our FedZSL protocol: 1) the trained models are prone to be biased to the locally observed classes, thus failing to generalize to the unseen classes and/or seen classes appeared on other devices; 2) as each category in the training data comes from a single source, the central model is highly vulnerable to model replacement (backdoor) attacks.

Federated Learning Zero-Shot Learning

GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning

no code implementations5 Jul 2022 Zhi Chen, Yadan Luo, Sen Wang, Jingjing Li, Zi Huang

To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.

Attribute Generalized Zero-Shot Learning

Graph Component Contrastive Learning for Concept Relatedness Estimation

1 code implementation25 Jun 2022 Yueen Ma, Zixing Song, Xuming Hu, Jingjing Li, Yifei Zhang, Irwin King

As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph.

Contrastive Learning Data Augmentation +2

Text Revision by On-the-Fly Representation Optimization

1 code implementation In2Writing (ACL) 2022 Jingjing Li, Zichao Li, Tao Ge, Irwin King, Michael R. Lyu

In this approach, we simply fine-tune a pre-trained Transformer with masked language modeling and attribute classification.

Attribute Language Modelling +3

A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model

no code implementations26 Jan 2022 Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, Houfeng Wang

Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns.

Grammatical Error Correction Language Modelling +3

Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization

no code implementations CVPR 2022 Jingjing Li, Tianyu Yang, Wei Ji, Jue Wang, Li Cheng

Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting.

Contrastive Learning Denoising +4

Distinguishing Unseen From Seen for Generalized Zero-Shot Learning

no code implementations CVPR 2022 Hongzu Su, Jingjing Li, Zhi Chen, Lei Zhu, Ke Lu

In this paper, we present a novel method which leverages both visual and semantic modalities to distinguish seen and unseen categories.

Generalized Zero-Shot Learning

Region Semantically Aligned Network for Zero-Shot Learning

no code implementations14 Oct 2021 Ziyang Wang, Yunhao Gou, Jingjing Li, Yu Zhang, Yang Yang

Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes.

Attribute Transfer Learning +1

Domain Adaptive Semantic Segmentation without Source Data

1 code implementation13 Oct 2021 Fuming You, Jingjing Li, Lei Zhu, Ke Lu, Zhi Chen, Zi Huang

To address these problems, we investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore.

Segmentation Semantic Segmentation

Test-time Batch Statistics Calibration for Covariate Shift

no code implementations6 Oct 2021 Fuming You, Jingjing Li, Zhou Zhao

An previous solution is test time normalization, which substitutes the source statistics in BN layers with the target batch statistics.

Domain Generalization Image Classification +3

Adversarial Energy Disaggregation for Non-intrusive Load Monitoring

no code implementations2 Aug 2021 Zhekai Du, Jingjing Li, Lei Zhu, Ke Lu, Heng Tao Shen

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis.

Non-Intrusive Load Monitoring

Spectrum Gaussian Processes Based On Tunable Basis Functions

no code implementations14 Jul 2021 Wenqi Fang, Guanlin Wu, Jingjing Li, Zheng Wang, Jiang Cao, Yang Ping

Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity.

Gaussian Processes

Mitigating Generation Shifts for Generalized Zero-Shot Learning

1 code implementation7 Jul 2021 Zhi Chen, Yadan Luo, Sen Wang, Ruihong Qiu, Jingjing Li, Zi Huang

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e. g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training.

Attribute Generalized Zero-Shot Learning

A convolutional neural network for prestack fracture detection

no code implementations3 Jul 2021 Zhenyu Yuan, Yuxin Jiang, Jingjing Li, Handong Huang

From prestack seismic gathers, anisotropic analysis and inversion were commonly applied to characterize the dominant orientations and relative intensities of fractures.

Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

no code implementations2 Jul 2021 Ruihong Qiu, Zi Huang, Jingjing Li, Hongzhi Yin

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i. e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest.

Representation Learning Session-Based Recommendations

Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos

no code implementations CVPR 2021 Mingxing Zhang, Yang Yang, Xinghan Chen, Yanli Ji, Xing Xu, Jingjing Li, Heng Tao Shen

Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation.

Sentence

Calibrated RGB-D Salient Object Detection

1 code implementation CVPR 2021 Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).

Object object-detection +3

Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

1 code implementation CVPR 2021 Zhekai Du, Jingjing Li, Hongzu Su, Lei Zhu, Ke Lu

Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers.

Clustering Self-Supervised Learning +1

Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel

no code implementations25 Feb 2021 Jingjing Li, Zhuo Sun, Lei Zhang, Hongyu Zhu

The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder.

Open-Retrieval Conversational Machine Reading

1 code implementation17 Feb 2021 Yifan Gao, Jingjing Li, Chien-Sheng Wu, Michael R. Lyu, Irwin King

On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin.

Discourse Segmentation Reading Comprehension +1

Semantics Disentangling for Generalized Zero-Shot Learning

1 code implementation ICCV 2021 Zhi Chen, Yadan Luo, Ruihong Qiu, Sen Wang, Zi Huang, Jingjing Li, Zheng Zhang

Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training.

Generalized Zero-Shot Learning Relation Network

Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning

no code implementations9 Jan 2021 Zhi Chen, Zi Huang, Jingjing Li, Zheng Zhang

To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes.

Generalized Zero-Shot Learning

BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification

no code implementations ICCV 2021 Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li

In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i. e., an elevated view of an object from above.

Person Re-Identification

Deep Pairwise Hashing for Cold-start Recommendation

no code implementations2 Nov 2020 Yan Zhang, Ivor W. Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, Jingjing Li

Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.

Denoising

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

no code implementations23 Oct 2020 Taotao Jing, Bingrong Xu, Jingjing Li, Zhengming Ding

Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge.

Domain Adaptation Fairness +1

Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

1 code implementation EMNLP 2020 Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C. H. Hoi, Caiming Xiong, Irwin King, Michael R. Lyu

Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information.

Decision Making Discourse Segmentation +3

Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches

no code implementations27 Jul 2020 Zhi Chen, Sen Wang, Jingjing Li, Zi Huang

A voting strategy averages the probability distributions output from the classifiers and, given that some patches are more discriminative than others, a discrimination-based attention mechanism helps to weight each patch accordingly.

Ensemble Learning Fine-Grained Image Classification +1

Accurate RGB-D Salient Object Detection via Collaborative Learning

2 code implementations ECCV 2020 Wei Ji, Jingjing Li, Miao Zhang, Yongri Piao, Huchuan Lu

The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.

Object object-detection +5

Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval

1 code implementation10 Jun 2020 Lei Zhu, Hui Cui, Zhiyong Cheng, Jingjing Li, Zheng Zhang

Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.

Deep Hashing Image Retrieval +1

Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs

no code implementations18 May 2020 Zhenyu Yuan, Yuxin Jiang, Jingjing Li, Handong Huang

Regarding as a combination of feature learning and target learning, the new proposed networks provide great capacity in high-hierarchy feature extraction and in-depth data mining.

Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation

no code implementations24 Mar 2020 Yang Xu, Lei Zhu, Zhiyong Cheng, Jingjing Li, Jiande Sun

Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations.

Collaborative Filtering Quantization

Memory-oriented Decoder for Light Field Salient Object Detection

1 code implementation NeurIPS 2019 Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu

In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection.

object-detection RGB Salient Object Detection +2

Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

1 code implementation27 Nov 2019 Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin

In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.

Graph Classification Session-Based Recommendations

Improving Question Generation With to the Point Context

no code implementations IJCNLP 2019 Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, Michael R. Lyu

Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence.

Question Generation Question-Generation +1

CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language

no code implementations21 Sep 2019 Zhi Chen, Jingjing Li, Yadan Luo, Zi Huang, Yang Yang

Thus, a multi-modal cycle-consistency loss between the synthesized semantic representations and the ground truth can be learned and leveraged to enforce the generated semantic features to approximate to the real distribution in semantic space.

Generative Adversarial Network Zero-Shot Learning

Alleviating Feature Confusion for Generative Zero-shot Learning

1 code implementation17 Sep 2019 Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, Zi Huang

An inevitable issue of such a paradigm is that the synthesized unseen features are prone to seen references and incapable to reflect the novelty and diversity of real unseen instances.

Generalized Zero-Shot Learning Test

Cycle-consistent Conditional Adversarial Transfer Networks

1 code implementation17 Sep 2019 Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, Zi Huang

Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions.

Domain Adaptation Transfer Learning

SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension

no code implementations28 Aug 2019 Jingjing Li, Wenlu Wang, Wei-Shinn Ku, Yingtao Tian, Haixun Wang

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS).

Management Reading Comprehension

Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation

no code implementations1 Aug 2019 Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Jingjing Li, Yang Yang

Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way.

Imitation Learning reinforcement-learning +1

Agile Domain Adaptation

no code implementations11 Jul 2019 Jingjing Li, Mengmeng Jing, Yue Xie, Ke Lu, Zi Huang

Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation.

Domain Adaptation

From Zero-Shot Learning to Cold-Start Recommendation

1 code implementation20 Jun 2019 Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, Zi Huang

This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR.

Recommendation Systems Zero-Shot Learning

A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset

no code implementations17 Jun 2019 Hanyu Li, Jingjing Li, Wei Wang

Underwater image enhancement algorithms have attracted much attention in underwater vision task.

Image Enhancement Test

Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder

1 code implementation3 Jun 2019 Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon

We analyze the histogram of the likelihoods of the input images using the generalized mean, which measures the model's accuracy as a function of the relative risk.

Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection

no code implementations25 Apr 2019 Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng

We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.

feature selection

Discrete Optimal Graph Clustering

1 code implementation25 Apr 2019 Yudong Han, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiaobai Liu

2) the relaxing process of cluster labels may cause significant information loss.

Clustering Graph Clustering +1

Leveraging the Invariant Side of Generative Zero-Shot Learning

1 code implementation CVPR 2019 Jingjing Li, Mengmeng Jin, Ke Lu, Zhengming Ding, Lei Zhu, Zi Huang

In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions.

Generalized Zero-Shot Learning

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