Search Results for author: Zi Huang

Found 59 papers, 21 papers with code

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation29 Mar 2022 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang

In this survey, a timely and systematical review of the research efforts on self-supervised recommendation (SSR) is presented.

Recommendation Systems Self-Supervised Learning

Source-Free Progressive Graph Learning for Open-Set Domain Adaptation

1 code implementation13 Feb 2022 Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, Mahsa Baktashmotlagh

However, most existing OSDA approaches are limited due to three main reasons, including: (1) the lack of essential theoretical analysis of generalization bound, (2) the reliance on the coexistence of source and target data during adaptation, and (3) failing to accurately estimate the uncertainty of model predictions.

Action Recognition Domain Adaptation +2

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

no code implementations13 Dec 2021 Yudi Li, Min Tang, Yun Yang, Zi Huang, Ruofeng Tong, Shuangcai Yang, Yao Li, Dinesh Manocha

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction.

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

no code implementations21 Oct 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, Lizhen Cui

Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

Federated Learning Model Poisoning +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.

Semantic Segmentation

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

2 code implementations12 Oct 2021 Ruihong Qiu, Zi Huang, Hongzhi Yin, Zijian Wang

In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution.

Contrastive Learning Sequential Recommendation

Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

2 code implementations1 Sep 2021 Ruihong Qiu, Zi Huang, Hongzhi Yin

In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec.

Contrastive Learning Sequential Recommendation

Learning to Diversify for Single Domain Generalization

1 code implementation ICCV 2021 Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, Mahsa Baktashmotlagh

Domain generalization (DG) aims to generalize a model trained on multiple source (i. e., training) domains to a distributionally different target (i. e., test) domain.

Domain Generalization

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.

Generalized Zero-Shot Learning

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

1 code implementation6 Jul 2021 Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang

Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation.

Counterfactual Inference Recommendation Systems

Attribute-aware Explainable Complementary Clothing Recommendation

no code implementations4 Jul 2021 Yang Li, Tong Chen, Zi Huang

As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process.

Recommendation Systems

Exploiting Positional Information for Session-based Recommendation

no code implementations2 Jul 2021 Ruihong Qiu, Zi Huang, Tong Chen, Hongzhi Yin

According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.

Session-Based Recommendations

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

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

no code implementations30 Jun 2021 Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, Zi Huang

Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.

Multi-Task Learning

Learning Elastic Embeddings for Customizing On-Device Recommenders

no code implementations4 Jun 2021 Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang

The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.

Recommendation Systems

Learning to Ask Appropriate Questions in Conversational Recommendation

no code implementations11 May 2021 Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng

Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.

Question Generation Recommendation Systems

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

no code implementations5 Apr 2021 Tong Chen, Hongzhi Yin, Xiangliang Zhang, Zi Huang, Yang Wang, Meng Wang

As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.

Feature Engineering

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

no code implementations4 Apr 2021 Tong Chen, Hongzhi Yin, Jie Ren, Zi Huang, Xiangliang Zhang, Hao Wang

In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.

Graph Representation Learning

Enhanced Modality Transition for Image Captioning

no code implementations23 Feb 2021 Ziwei Wang, Yadan Luo, Zi Huang

In this work, we explicitly build a Modality Transition Module (MTM) to transfer visual features into semantic representations before forwarding them to the language model.

Image Captioning Language Modelling +1

Graph Embedding for Recommendation against Attribute Inference Attacks

no code implementations29 Jan 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang

Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.

Graph Embedding Recommendation Systems

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

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

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph

1 code implementation25 Nov 2020 Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Mahsa Baktashmotlagh

Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task.

Link Prediction

Adversarial Bipartite Graph Learning for Video Domain Adaptation

1 code implementation31 Jul 2020 Yadan Luo, Zi Huang, Zijian Wang, Zheng Zhang, Mahsa Baktashmotlagh

To further enhance the model capacity and testify the robustness of the proposed architecture on difficult transfer tasks, we extend our model to work in a semi-supervised setting using an additional video-level bipartite graph.

Domain Adaptation Graph Learning +1

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

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

1 code implementation6 Jul 2020 Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen

On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.

Session-Based Recommendations

Progressive Graph Learning for Open-Set Domain Adaptation

1 code implementation ICML 2020 Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh

The existing domain adaptation approaches which tackle this problem work in the closed-set setting with the assumption that the source and the target data share exactly the same classes of objects.

Domain Adaptation Graph Learning

ORD: Object Relationship Discovery for Visual Dialogue Generation

no code implementations15 Jun 2020 Ziwei Wang, Zi Huang, Yadan Luo, Huimin Lu

With the rapid advancement of image captioning and visual question answering at single-round level, the question of how to generate multi-round dialogue about visual content has not yet been well explored. Existing visual dialogue methods encode the image into a fixed feature vector directly, concatenated with the question and history embeddings to predict the response. Some recent methods tackle the co-reference resolution problem using co-attention mechanism to cross-refer relevant elements from the image, history, and the target question. However, it remains challenging to reason visual relationships, since the fine-grained object-level information is omitted before co-attentive reasoning.

Dialogue Generation Graph Attention +4

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

1 code implementation20 May 2020 Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui

Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks.

Recommendation Systems Representation Learning

Try This Instead: Personalized and Interpretable Substitute Recommendation

no code implementations19 May 2020 Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang

Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.

Collaborative Filtering Sentiment Analysis

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

no code implementations16 Apr 2020 Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations5 Apr 2020 Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.

Recommendation Systems

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

Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling

no code implementations12 Nov 2019 Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang

Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data.

Continual Learning Few-Shot Learning

Deep Collaborative Discrete Hashing with Semantic-Invariant Structure

no code implementations5 Nov 2019 Zijian Wang, Zheng Zhang, Yadan Luo, Zi Huang

Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance.

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.

Zero-Shot Learning

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

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

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.

Decision Making Imitation 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

Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

no code implementations25 Apr 2019 Lei Zhu, Zi Huang, Zhihui Li, Liang Xie, Heng Tao Shen

To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH).

Content-Based Image Retrieval

Leveraging the Invariant Side of Generative Zero-Shot Learning

2 code implementations 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

Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval

no code implementations5 Apr 2019 Yadan Luo, Ziwei Wang, Zi Huang, Yang Yang, Huimin Lu

With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items.

Image Retrieval

SADIH: Semantic-Aware DIscrete Hashing

no code implementations3 Apr 2019 Zheng Zhang, Guo-Sen Xie, Yang Li, Sheng Li, Zi Huang

Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.

Learning Private Neural Language Modeling with Attentive Aggregation

4 code implementations17 Dec 2018 Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang

Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.

Federated Learning Language Modelling

Look Deeper See Richer: Depth-aware Image Paragraph Captioning

no code implementations ACM International Conference on Multimedia 2018 Ziwei Wang, Yadan Luo, Yang Li, Zi Huang, Hongzhi Yin

Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.

Image Captioning Image Paragraph Captioning

Collaborative Learning for Extremely Low Bit Asymmetric Hashing

1 code implementation25 Sep 2018 Yadan Luo, Zi Huang, Yang Li, Fumin Shen, Yang Yang, Peng Cui

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.

Image Retrieval

Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

no code implementations22 Aug 2018 Yadan Luo, Ziwei Wang, Zi Huang, Yang Yang, Cong Zhao

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming.

Semantic Segmentation Weakly supervised segmentation

Leveraging Weak Semantic Relevance for Complex Video Event Classification

no code implementations ICCV 2017 Chao Li, Jiewei Cao, Zi Huang, Lei Zhu, Heng Tao Shen

In this paper, we propose a novel approach to automatically maximize the utility of weak semantic annotations (formalized as the semantic relevance of video shots to the target event) to facilitate video event classification.

Classification General Classification

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

no code implementations13 Jul 2017 Lei Zhu, Zi Huang, Xiaobai Liu, Xiangnan He, Jingkuan Song, Xiaofang Zhou

Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.

Multi-Attention Network for One Shot Learning

no code implementations CVPR 2017 Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton Van Den Hengel, Heng Tao Shen

One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image.

One-Shot Learning TAG +1

From Community Detection to Community Profiling

no code implementations17 Jan 2017 Hongyun Cai, Vincent W. Zheng, Fanwei Zhu, Kevin Chen-Chuan Chang, Zi Huang

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.

Community Detection

Binary Subspace Coding for Query-by-Image Video Retrieval

no code implementations6 Dec 2016 Ruicong Xu, Yang Yang, Yadan Luo, Fumin Shen, Zi Huang, Heng Tao Shen

The first approach, termed Inner-product Binary Coding (IBC), preserves the inner relationships of images and videos in a common Hamming space.

Video Retrieval

Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps

no code implementations22 Jun 2016 Jiewei Cao, Lingqiao Liu, Peng Wang, Zi Huang, Chunhua Shen, Heng Tao Shen

Instance retrieval requires one to search for images that contain a particular object within a large corpus.

What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution

no code implementations CVPR 2016 Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton Van Den Hengel, Heng Tao Shen

The key observation motivating our approach is that "regular object" images, "unusual object" images and "other objects" images exhibit different region-level scores in terms of both the score values and the spatial distributions.

Gaussian Processes Object Detection

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