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        <title>Bridging the Gap between Authentic and Answer-Guided Images for Chinese Vision-Language Understanding Enhancement</title>
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        <namePart type="given">Feiyu</namePart>
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        <namePart type="given">Wenyu</namePart>
        <namePart type="family">Guo</namePart>
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        <namePart type="given">Pengyuan</namePart>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
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            <namePart type="given">Lin</namePart>
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            <namePart type="given">Tan</namePart>
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    <abstract>“The objective of the Chinese Vision-Language Understanding Evaluation (CVLUE) is to comprehensively assess the performance of Chinese vision-language multimodal pre-trained models in multimodal modeling and understanding across four tasks: Image-Text Retrieval, Visual Question Answering, Visual Grounding, and Visual Dialog. To enhance the models’ performance across various multimodal tasks, this paper propose a multimodal information understanding enhancement method based on answer-guided images. Firstly, we propose task-specific methods for answer-guided image generation. Secondly, the authentic and answer-guided images are fed into the model for multimodal fine-tuning, respectively. Finally, training objectives are set for different tasks to minimize the gap between the answer-guided images and authentic images, thereby supervising the results produced by the authentic images utlizing answer-guided images. The experimental results demonstrate the effectiveness of the proposed method.”</abstract>
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        <date>2024-07</date>
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            <start>353</start>
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%0 Conference Proceedings
%T Bridging the Gap between Authentic and Answer-Guided Images for Chinese Vision-Language Understanding Enhancement
%A Wang, Feiyu
%A Guo, Wenyu
%A Yu, Dong
%A Kang, Chen
%A Liu, Pengyuan
%Y Hongfei, Lin
%Y Hongye, Tan
%Y Bin, Li
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F feiyu-etal-2024-bridging
%X “The objective of the Chinese Vision-Language Understanding Evaluation (CVLUE) is to comprehensively assess the performance of Chinese vision-language multimodal pre-trained models in multimodal modeling and understanding across four tasks: Image-Text Retrieval, Visual Question Answering, Visual Grounding, and Visual Dialog. To enhance the models’ performance across various multimodal tasks, this paper propose a multimodal information understanding enhancement method based on answer-guided images. Firstly, we propose task-specific methods for answer-guided image generation. Secondly, the authentic and answer-guided images are fed into the model for multimodal fine-tuning, respectively. Finally, training objectives are set for different tasks to minimize the gap between the answer-guided images and authentic images, thereby supervising the results produced by the authentic images utlizing answer-guided images. The experimental results demonstrate the effectiveness of the proposed method.”
%U https://aclanthology.org/2024.ccl-3.40/
%P 353-362

Markdown (Informal)

[Bridging the Gap between Authentic and Answer-Guided Images for Chinese Vision-Language Understanding Enhancement](https://aclanthology.org/2024.ccl-3.40/) (Wang et al., CCL 2024)

• Bridging the Gap between Authentic and Answer-Guided Images for Chinese Vision-Language Understanding Enhancement (Wang et al., CCL 2024)

ACL

• Feiyu Wang, Wenyu Guo, Dong Yu, Chen Kang, and Pengyuan Liu. 2024. Bridging the Gap between Authentic and Answer-Guided Images for Chinese Vision-Language Understanding Enhancement. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 353–362, Taiyuan, China. Chinese Information Processing Society of China.