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Found 41 papers, 18 papers with code
基于有向异构图的发票明细税收分类方法(Tax Classification of Invoice Details Based on Directed Heterogeneous Graph)
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。
A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels
Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data.
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts
For instance, the metadata and the corresponding user's information of a review could be helpful.
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
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.
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection.
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts.
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
Disentangled Representation Learning with Transmitted Information Bottleneck
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Supplementary experiments further validate the effectiveness of our model design and the necessity of each module.
Adversarial Attacks on Fairness of Graph Neural Networks
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.
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
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).
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls.
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance.
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
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.
Noisy Correspondence Learning with Meta Similarity Correction
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data.
Disentangled Generation with Information Bottleneck for Few-Shot Learning
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.
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
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.
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
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.
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
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.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
Towards Explanation for Unsupervised Graph-Level Representation Learning
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?}"
Noise-Tolerant Learning for Audio-Visual Action Recognition
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.
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
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.
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
On the other hand, there are numerous unpaired persons in real-world scene images.
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
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.
PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method
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.
Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding
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.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
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
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
Towards Entity Alignment in the Open World: An Unsupervised Approach
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.
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
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.
Scalable Attack on Graph Data by Injecting Vicious Nodes
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.
ZSTAD: Zero-Shot Temporal Activity Detection
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
Self-Supervised Graph Representation Learning via Global Context Prediction
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.
Graph Representation Learning via Graphical Mutual Information Maximization
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.
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval.
