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Chong Chen

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17 papers
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17

TIST Journal 2025 Journal Article

A GPT-assisted Multi-Granularity Contrastive Learning approach for Knowledge Graph Entity Typing

  • Hongbin Zhang
  • Tao Wang
  • Zhuowei Wang
  • Nankai Lin
  • Chong Chen
  • Lianglun Cheng

Knowledge graph entity typing (KGET) is an efficient way to infer possible missing types for entities, which has become a key instrument to enhance the construction of knowledge graphs (KGs). Existing models to KGET have mainly focused on a single granularity information such as distinct entity information, but other granularity information including entity-to-type-clusters, the same cluster and interaction information have not been fully explored, resulting in inferring incorrect types in KGs. To address this, we propose a GPT-assisted Multi-Granularity Contrastive Learning (GMGCL) approach to acquire entity-to-type-clusters, entity, type-cluster and relation information by GPT-assisted entity-to-type-clusters clustering, entity-based, cluster-based and relation-based contrastive learning, respectively. Our approach is evaluated on FB15kET and YAGO43kET datasets, outperforming other baselines and obtaining a 1.35% average improvement at least on MRR.

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AAAI Conference 2025 Conference Paper

DREAM: Decoupled Discriminative Learning with Bigraph-aware Alignment for Semi-supervised 2D-3D Cross-modal Retrieval

  • Fan Zhang
  • Changhu Wang
  • Zebang Cheng
  • Xiaojiang Peng
  • Dongjie Wang
  • Yijia Xiao
  • Chong Chen
  • Xian-Sheng Hua

With the burst of big data, 2D-3D cross-modal retrieval has received increasing attention, which aims to retrieve relevant data from one modality given the query from the other modality. In this paper, we study an underexplored yet practical problem of semi-supervised 2D-3D cross-modal retrieval, which could suffer from serious label scarcity in real-world applications. Moreover, the huge heterogeneous gap could deteriorate the process of learning from unlabeled data. In this work, we propose a novel approach named Decoupled Discriminative Learning with Bigraph-aware Alignment (DREAM) for semi-supervised 2D-3D cross-modal retrieval. The core of our DREAM is to decouple the label prediction and reliability measurement processes to reduce overconfident samples in discriminative learning. In particular, we enhance a label prediction module with label propagation from labeled samples and additionally introduce a reliability measurement module to learn the scores of predicted labels. To reduce class-related bias, we compare reliability scores with class-specific adaptive thresholds to identify samples for additional learning. In addition, negative labels are estimated for unselected samples, which guides soft semantic learning to make the best use of all the information. To further minimize the heterogeneous gap, we build a bigraph graph that connects cross-modal similar examples and then conduct learning to cluster with most edges kept for alignment. Extensive experiments on several benchmark datasets validate the superiority of the proposed DREAM.

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AAAI Conference 2025 Conference Paper

SongSong: A Time Phonograph for Chinese SongCi Music from Thousand of Years Away

  • Jiliang Hu
  • Jiajia Li
  • Ziyi Pan
  • Chong Chen
  • Zuchao Li
  • Ping Wang
  • Lefei Zhang

Recently, there have been significant advancements in music generation. However, existing models primarily focus on creating modern pop songs, making it challenging to produce ancient music with distinct rhythms and styles, such as ancient Chinese SongCi. In this paper, we introduce SongSong, the first music generation model capable of restoring Chinese SongCi to our knowledge. Our model first predicts the melody from the input SongCi, then separately generates the singing voice and accompaniment based on that melody, and finally combines all elements to create the final piece of music. Additionally, to address the lack of ancient music datasets, we create OpenSongSong, a comprehensive dataset of ancient Chinese SongCi music, featuring 29.9 hours of compositions by various renowned SongCi music masters. To assess SongSong's proficiency in performing SongCi, we randomly select 85 SongCi sentences that were not part of the training set for evaluation against SongSong and music generation platforms such as Suno and SkyMusic. The subjective and objective outcomes indicate that our proposed model achieves leading performance in generating high-quality SongCi music.

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NeurIPS Conference 2024 Conference Paper

PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling

  • Hao Wu
  • Changhu Wang
  • Fan Xu
  • Jinbao Xue
  • Chong Chen
  • Xian-Sheng Hua
  • Xiao Luo

This work studies the problem of out-of-distribution fluid dynamics modeling. Previous works usually design effective neural operators to learn from mesh-based data structures. However, in real-world applications, they would suffer from distribution shifts from the variance of system parameters and temporal evolution of the dynamical system. In this paper, we propose a novel approach named \underline{P}rompt Evol\underline{u}tion with G\underline{r}aph OD\underline{E} (\method{}) for out-of-distribution fluid dynamics modeling. The core of our \method{} is to learn time-evolving prompts using a graph ODE to adapt spatio-temporal forecasting models to different scenarios. In particular, our \method{} first learns from historical observations and system parameters in the frequency domain to explore multi-view context information, which could effectively initialize prompt embeddings. More importantly, we incorporate the interpolation of observation sequences into a graph ODE, which can capture the temporal evolution of prompt embeddings for model adaptation. These time-evolving prompt embeddings are then incorporated into basic forecasting models to overcome temporal distribution shifts. We also minimize the mutual information between prompt embeddings and observation embeddings to enhance the robustness of our model to different distributions. Extensive experiments on various benchmark datasets validate the superiority of the proposed \method{} in comparison to various baselines.

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NeurIPS Conference 2024 Conference Paper

Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data

  • Fan Zhang
  • Tianyu Liu
  • Zihao Chen
  • Xiaojiang Peng
  • Chong Chen
  • Xian-Sheng Hua
  • Xiao Luo
  • Hongyu Zhao

Knowledge transfer between multi-omic single-cell data aims to effectively transfer cell types from scRNA-seq data to unannotated scATAC-seq data. Several approaches aim to reduce the heterogeneity of multi-omic data while maintaining the discriminability of cell types with extensive annotated data. However, in reality, the cost of collecting both a large amount of labeled scRNA-seq data and scATAC-seq data is expensive. Therefore, this paper explores a practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose a semi-supervised knowledge transfer framework named Dual label scArcity elimiNation with Cross-omic multi-samplE Mixup (DANCE). To overcome the label scarcity in scRNA-seq data, we generate pseudo-labels based on optimal transport and merge them into the labeled scRNA-seq data. Moreover, we adopt a divide-and-conquer strategy which divides the scATAC-seq data into source-like and target-specific data. For source-like samples, we employ consistency regularization with random perturbations while for target-specific samples, we select a few candidate labels and progressively eliminate incorrect cell types from the label set for additional supervision. Next, we generate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments on many benchmark datasets suggest the superiority of our DANCE over a series of state-of-the-art methods.

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IROS Conference 2023 Conference Paper

A Minimal Collision Strategy of Synergy Between Pushing and Grasping for Large Clusters of Objects

  • Chong Chen
  • Shijun Yan
  • Miaolong Yuan
  • Chiat-Pin Tay
  • Dongkyu Choi
  • Quang Dan Le

Grasping and moving objects in a large cluster is a common real scenario. In such scenarios, tens of objects are adjacent to each other, even stacked layer by layer, so that simple grasp would not work due to obstruction. In this paper, we propose a well-designed strategy to use synergy of pushing and grasping to automatically push and grasp objects in a large tightly packed cluster of objects. Our strategy is to detect and grasp isolated graspable objects first before other actions. We then use a smart strategy that pushes objects at the narrowest edge of the clusters. For push action, the robot pushes the edge at the perpendicular direction relative to the cluster, thus improving the performance of isolation and minimizing collisions. We have conducted experiments in both simulation and real-world environments with more than 20 cluttered objects and demonstrated that our solution outperforms existing deep learning based methods, especially in challenging cases, and achieves significantly higher completion rate, grasp success rate, picked rate and efficiency.

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NeurIPS Conference 2023 Conference Paper

IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval

  • Haixin Wang
  • Hao Wu
  • Jinan Sun
  • Shikun Zhang
  • Chong Chen
  • Xian-Sheng Hua
  • Xiao Luo

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which leverage knowledge from a label-rich source domain to expedite learning to hash on a label-scarce target domain. Although numerous existing approaches attempt to incorporate transfer learning techniques into deep hashing frameworks, they often neglect the essential invariance for adequate alignment between these two domains. Worse yet, these methods fail to distinguish between causal and non-causal effects embedded in images, rendering cross-domain retrieval ineffective. To address these challenges, we propose an Invariance-acquired Domain AdaptivE HAshing (IDEA) model. Our IDEA first decomposes each image into a causal feature representing label information, and a non-causal feature indicating domain information. Subsequently, we generate discriminative hash codes using causal features with consistency learning on both source and target domains. More importantly, we employ a generative model for synthetic samples to simulate the intervention of various non-causal effects, ultimately minimizing their impact on hash codes for domain invariance. Comprehensive experiments conducted on benchmark datasets validate the superior performance of our IDEA compared to a variety of competitive baselines.

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IJCAI Conference 2022 Conference Paper

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

  • Wei Ju
  • Xiao Luo
  • Meng Qu
  • Yifan Wang
  • Chong Chen
  • Minghua Deng
  • Xian-Sheng Hua
  • Ming Zhang

This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-level representations for classification, failing to explicitly leverage features derived from graph topology (e. g. , paths). Moreover, when labeled data is scarce, these methods are far from satisfactory due to their insufficient topology exploration of unlabeled data. We address the challenge by proposing a novel semi-supervised framework called Twin Graph Neural Network (TGNN). To explore graph structural information from complementary views, our TGNN has a message passing module and a graph kernel module. To fully utilize unlabeled data, for each module, we calculate the similarity of each unlabeled graph to other labeled graphs in the memory bank and our consistency loss encourages consistency between two similarity distributions in different embedding spaces. The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data. We evaluate our TGNN on various public datasets and show that it achieves strong performance.

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IJCAI Conference 2021 Conference Paper

CIMON: Towards High-quality Hash Codes

  • Xiao Luo
  • Daqing Wu
  • Zeyu Ma
  • Chong Chen
  • Minghua Deng
  • Jinwen Ma
  • Zhongming Jin
  • Jianqiang Huang

Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i. e. , treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.

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AAAI Conference 2021 Conference Paper

Graph Heterogeneous Multi-Relational Recommendation

  • Chong Chen
  • Weizhi Ma
  • Min Zhang
  • Zhaowei Wang
  • Xiuqiang He
  • Chenyang Wang
  • Yiqun Liu
  • Shaoping Ma

Traditional studies on recommender systems usually leverage only one type of user behaviors (the optimization target, such as purchase), despite the fact that users also generate a large number of various types of interaction data (e. g. , view, click, add-to-cart, etc). Generally, these heterogeneous multirelational data provide well-structured information and can be used for high-quality recommendation. Early efforts towards leveraging these heterogeneous data fail to capture the high-hop structure of user-item interactions, which are unable to make full use of them and may only achieve constrained recommendation performance. In this work, we propose a new multi-relational recommendation model named Graph Heterogeneous Collaborative Filtering (GHCF). To explore the high-hop heterogeneous user-item interactions, we take the advantages of Graph Convolutional Network (GCN) and further improve it to jointly embed both representations of nodes (users and items) and relations for multi-relational prediction. Moreover, to fully utilize the whole heterogeneous data, we perform the advanced efficient non-sampling optimization under a multi-task learning framework. Experimental results on two public benchmarks show that GHCF significantly outperforms the state-of-the-art recommendation methods, especially for cold-start users who have few primary item interactions. Further analysis verifies the importance of the proposed embedding propagation for modelling high-hop heterogeneous user-item interactions, showing the rationality and effectiveness of GHCF. Our implementation has been released (https: //github. com/chenchongthu/GHCF).

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AAAI Conference 2020 Conference Paper

Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

  • Chong Chen
  • Min Zhang
  • Yongfeng Zhang
  • Weizhi Ma
  • Yiqun Liu
  • Shaoping Ma

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e. g. , view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel nonsampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training ef- ficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

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