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Relevant visual Semantic Context-Aware Attention-Based dialog

Computers, Materials & Continua 2023年第8期76卷 2337-2354页

作者： Eugene Tan Boon Hong Yung-Wey Chong Tat-Chee Wan Kok-Lim Alvin Yau National Advanced IPv6 Centre Universiti Sains MalaysiaPenangMalaysia Lee Kong Chian Faculty of Engineering and Science(LKCFES) Universiti Tunku Abdul RahmanSungai LongSelangorMalaysia

The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image ***,it faces challenges in overcoming visual semantic limitations,particularly in obtaining sufficient context from visual and textual aspects of *** paper proposes a new visual dialog dataset called Diverse History-dialog(DS-dialog)to address the visual semantic limitations faced by the existing ***-dialog groups relevant histories based on their respective Microsoft Common Objects in Context(MSCOCO)image categories and consolidates them for each ***,each MSCOCO image category consists of top relevant histories extracted based on their semantic relationships between the original image caption and historical *** relevant histories are consolidated for each image,and DS-dialog enhances the current dataset by adding new context-aware relevant history to provide more visual semantic context for each *** new dataset is generated through several stages,including image semantic feature extraction,keyphrase extraction,relevant question extraction,and relevant history dialog *** DS-dialog dataset contains about 2.6 million question-answer pairs,where 1.3 million pairs correspond to existing VisDial’s question-answer pairs,and the remaining 1.3 million pairs include a maximum of 5 image features for each VisDial image,with each image comprising 10-round relevant question-answer ***,a novel adaptive relevant history selection is proposed to resolve missing visual semantic information for each ***-dialog is used to benchmark the performance of previous visual dialog models and achieves better performance than previous ***,the proposed DSdialog model achieves an 8% higher mean reciprocal rank(MRR),11% higher R@1%,6% higher R@5%,5% higher R@10%,and 8% higher normalized discounted cumulative gain(NDCG)compared to ***-dialog also achieves approximately 1 point improvement o

关键词： visual dialog context-aware relevant history computer vision natural language processing

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Coreference resolution helps visual dialogs to focus

High-Confidence Computing

引用

High-Confidence Computing 2024年第2期4卷 129-135页

作者： Tianwei Yue Wenping Wang Chen Liang Dachi Chen Congrui Hetang Xuewei Wang Carnegie Mellon University Pittsburgh 15213USA

visual dialog is a multi-modal task involving both computer vision and dialog *** goal is to answer multiple questions in conversation style,given an image as the *** networks with attention modules are widely used for this task,because of their effectiveness in reasoning the relevance between the texts and *** this work,we study how to further improve the quality of such reasoning,which is an open *** baseline is the Recursive visual Attention(RVA)model,which refines the vision-text attention by iteratively visiting the dialog *** on top of that,we propose to improve the attention mechanism with contrastive *** train a Matching-Aware Attention Kernel(MAAK)by aligning the deep feature embeddings of an image and its caption,to provide better attention *** show consistent improvements from *** addition,we study the effect of using Multimodal Compact Bilinear(MCB)pooling as a three-way feature fusion for the visual,textual and dialog history *** analyze the performance of both methods in the discussion section,and propose further ideas to resolve current limitations.

关键词： Multi-model machine learning visual dialog Co-reference resolution

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