720 citations · 756 across the 12 of their papers we have counts for
12 papers
CGMI: Configurable General Multi-Agent Interaction Framework
Shi Jinxin, Zhao Jiabao, Wang Yilei +3
Benefiting from the powerful capabilities of large language models (LLMs), agents based on LLMs have shown the potential to address domain-specific tasks and emulate human behavior…
FairMonitor: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models
Yanhong Bai, Jiabao Zhao, Jinxin Shi +3
Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied. However, the…
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Xin Li, Tao Ma, Yuenan Hou +8
LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and poi…
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
Xin Li, Botian Shi, Yuenan Hou +4
Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D…
A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis
Qi Zhang, Jie Zhou, Qin Chen +3
Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from b…
Enhancing Event-Level Sentiment Analysis with Structured Arguments
Qi Zhang, Jie Zhou, Qin Chen +2
Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, ti…
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis
Luwei Xiao, Xingjiao Wu, Wen Wu +2
Nowadays, with the explosive growth of multimodal reviews on social media platforms, multimodal sentiment analysis has recently gained popularity because of its high relevance to t…
Cross-Domain Document Layout Analysis Using Document Style Guide
Xingjiao Wu, Luwei Xiao, Xiangcheng Du +5
The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with st…
Long-Tail Session-based Recommendation from Calibration
Jiayi Chen, Wen Wu, Wei Zheng +1
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias. Existing models for…
Human-In-The-Loop Document Layout Analysis
Xingjiao Wu, Tianlong Ma, Xin Li +2
Document layout analysis (DLA) aims to divide a document image into different types of regions. DLA plays an important role in the document content understanding and information ex…
A Survey of Human-in-the-loop for Machine Learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun +3
Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning…
Attention Incorporate Network: A network can adapt various data size
Liangbo He, Hao Sun
In traditional neural networks for image processing, the inputs of the neural networks should be the same size such as 224*224*3. But how can we train the neural net model with dif…