Online dynamic working-state recognition through uncertain data classification

Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as tables, charts, and text. Most existing researches in MMQA only focus on two modalities such as image-text QA, table-text QA and chart-text QA, and there remains a notable scarcity in studies that investigate the joint analysis of text, tables, and charts. In this paper, we present CT 2 C-QA, a pioneering Chinese reasoning-based QA dataset that includes an extensive collection of text, tables, and charts, meticulously compiled from 200 selectively sourced webpages. Our dataset simulates real webpages and serves as a great test for the capability of the model to analyze and reason with multimodal data, because the answer to a question could appear in various modalities, or even potentially not exist at all. Additionally, we present AED (Allocating, Expert and Desicion), a multi-agent system implemented through collaborative deployment, information interaction, and collective decision-making among different agents. Specifically, the Assignment Agent is in charge of selecting and activating expert agents, including those proficient in text, tables, and charts. The Decision Agent bears the responsibility of delivering the final verdict, drawing upon the analytical insights provided by these expert agents. We execute a comprehensive analysis, comparing AED with various

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Online dynamic working-state recognition through uncertain data classification

Cited by 3 publications

References 19 publications

Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based Convolutional neural network for robust and scalable solutions in High-Dimensional data

Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based Convolutional neural network for robust and scalable solutions in High-Dimensional data

Clustering probabilistic graphs using neighbourhood paths

CT ² C-QA: Multimodal Question Answering over Chinese Text, Table and Chart

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Online dynamic working-state recognition through uncertain data classification

Cited by 3 publications

References 19 publications

Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based Convolutional neural network for robust and scalable solutions in High-Dimensional data

Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based Convolutional neural network for robust and scalable solutions in High-Dimensional data

Clustering probabilistic graphs using neighbourhood paths

CT 2 C-QA: Multimodal Question Answering over Chinese Text, Table and Chart

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Product

Resources

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CT ² C-QA: Multimodal Question Answering over Chinese Text, Table and Chart