no code implementations • CCL 2020 • Peiyao Zhao, Qinghua Zheng, Bo Dong, Jianfei Ruan, Minnan Luo
税收是国家赖以生存的物质基础。为加快税收现代化, 方便纳税人便捷、规范开具增值税发票, 国税总局规定纳税人在税控系统开票前选择发票明细对应的税收分类才可正常开具发票。提高税收分类的准确度, 是构建税收风险指标和分析纳税人行为特征的重要基础。基于此, 本文提出了一种基于有向异构图的短文本分类模型(Heterogeneous Directed Graph Attenton Network, HDGAT), 利用发票明细间的有向信息建模, 引入外部知识, 显著地提高了发票明细的税收分类准确度。
no code implementations • 20 Mar 2024 • Haochen Han, Minnan Luo, Huan Liu, Fang Nan
Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data.
1 code implementation • 8 Mar 2024 • Haochen Han, Qinghua Zheng, Guang Dai, Minnan Luo, Jingdong Wang
To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
no code implementations • 8 Mar 2024 • Zinan Zeng, Sen Ye, Zijian Cai, Heng Wang, YuHan Liu, Haokai Zhang, Minnan Luo
For instance, the metadata and the corresponding user's information of a review could be helpful.
no code implementations • 16 Feb 2024 • Herun Wan, Shangbin Feng, Zhaoxuan Tan, Heng Wang, Yulia Tsvetkov, Minnan Luo
Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount.
no code implementations • 1 Feb 2024 • Shangbin Feng, Herun Wan, Ningnan Wang, Zhaoxuan Tan, Minnan Luo, Yulia Tsvetkov
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection.
no code implementations • 27 Dec 2023 • Zhuohang Dang, Minnan Luo, Chengyou Jia, Guang Dai, Xiaojun Chang, Jingdong Wang
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
no code implementations • 4 Dec 2023 • Chengyou Jia, Minnan Luo, Xiaojun Chang, Zhuohang Dang, Mingfei Han, Mengmeng Wang, Guang Dai, Sizhe Dang, Jingdong Wang
To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts.
no code implementations • 8 Nov 2023 • Wujiang Xu, Xuying Ning, Wenfang Lin, Mingming Ha, Qiongxu Ma, Qianqiao Liang, Xuewen Tao, Linxun Chen, Bing Han, Minnan Luo
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
no code implementations • 3 Nov 2023 • Zhuohang Dang, Minnan Luo, Chengyou Jia, Guang Dai, Jihong Wang, Xiaojun Chang, Jingdong Wang, Qinghua Zheng
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
no code implementations • 25 Oct 2023 • Shiqi Lou, Qingyue Zhang, Shujie Yang, Yuyang Tian, Zhaoxuan Tan, Minnan Luo
Supplementary experiments further validate the effectiveness of our model design and the necessity of each module.
1 code implementation • 20 Oct 2023 • Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li
Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e. g., female) in graph-based applications.
no code implementations • 20 Sep 2023 • Chengyou Jia, Minnan Luo, Zhuohang Dang, Guang Dai, Xiaojun Chang, Jingdong Wang
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentification (ReID).
no code implementations • 20 Aug 2023 • Chengyou Jia, Minnan Luo, Zhuohang Dang, Guang Dai, Xiaojun Chang, Mengmeng Wang, Jingdong Wang
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls.
1 code implementation • 30 Jun 2023 • Zijian Cai, Zhaoxuan Tan, Zhenyu Lei, Zifeng Zhu, Hongrui Wang, Qinghua Zheng, Minnan Luo
For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance.
1 code implementation • 22 Apr 2023 • Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo
We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms.
1 code implementation • CVPR 2023 • Haochen Han, Kaiyao Miao, Qinghua Zheng, Minnan Luo
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data.
no code implementations • 29 Nov 2022 • Zhuohang Dang, Jihong Wang, Minnan Luo, Chengyou Jia, Caixia Yan, Qinghua Zheng
To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples.
1 code implementation • 15 Oct 2022 • Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction.
1 code implementation • 18 Aug 2022 • Xinshun Feng, Herun Wan, Shangbin Feng, Hongrui Wang, Jun Zhou, Qinghua Zheng, Minnan Luo
Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture.
1 code implementation • 17 Aug 2022 • Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.
1 code implementation • 17 Aug 2022 • Shujie Yang, Binchi Zhang, Shangbin Feng, Zhaoxuan Tan, Qinghua Zheng, Jun Zhou, Minnan Luo
In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.
1 code implementation • 16 Aug 2022 • Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
1 code implementation • 9 Jun 2022 • Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang, Zilong Chen, Binchi Zhang, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, YuHan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
1 code implementation • 20 May 2022 • Qinghua Zheng, Jihong Wang, Minnan Luo, YaoLiang Yu, Jundong Li, Lina Yao, Xiaojun Chang
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"
no code implementations • 16 May 2022 • Haochen Han, Qinghua Zheng, Minnan Luo, Kaiyao Miao, Feng Tian, Yan Chen
To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence.
1 code implementation • NAACL 2022 • Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, Minnan Luo
Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles.
no code implementations • 27 Mar 2022 • Chengyou Jia, Minnan Luo, Caixia Yan, Xiaojun Chang, Qinghua Zheng
On the other hand, there are numerous unpaired persons in real-world scene images.
no code implementations • 21 Jan 2022 • Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu, Jun Zhou, Qinghua Zheng
Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.
no code implementations • 22 Oct 2021 • Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, Qinghua Zheng
In light of these problems, we propose a Privacy-Preserving Subgraph sampling based distributed GCN training method (PPSGCN), which preserves data privacy and significantly cuts back on communication and memory overhead.
no code implementations • 12 Oct 2021 • Minnan Luo, Xiaojun Chang, Chen Gong
In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance.
no code implementations • 4 Sep 2021 • Caixia Yan, Xiaojun Chang, Minnan Luo, Huan Liu, Xiaoqin Zhang, Qinghua Zheng
To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.
Ranked #4 on Zero-Shot Object Detection on MS-COCO
Contrastive Learning Generalized Zero-Shot Object Detection +3
1 code implementation • 9 Aug 2021 • Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
1 code implementation • 9 Aug 2021 • Shangbin Feng, Zilong Chen, Wenqian Zhang, Qingyao Li, Qinghua Zheng, Xiaojun Chang, Minnan Luo
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
1 code implementation • 26 Jan 2021 • Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xinyi Li, Minnan Luo, Qinghua Zheng
These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment.
no code implementations • 24 Sep 2020 • Caixia Yan, Xiaojun Chang, Minnan Luo, Qinghua Zheng, Xiaoqin Zhang, Zhihui Li, Feiping Nie
In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.
1 code implementation • 22 Apr 2020 • Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, Qinghua Zheng
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.
no code implementations • CVPR 2020 • Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, ZongYuan Ge, Alexander Hauptmann
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
no code implementations • 3 Mar 2020 • Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, Qinghua Zheng
To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself.
1 code implementation • 4 Feb 2020 • Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang
The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.
no code implementations • 4 Feb 2017 • Minnan Luo, Xiaojun Chang, Zhihui Li, Liqiang Nie, Alexander G. Hauptmann, Qinghua Zheng
The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval.