Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918132965179392 |
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| author | Wu, Runtong Song, Jiayao Teng, Fei Ren, Xianhao Gao, Yuyan Yang, Kailun |
| author_facet | Wu, Runtong Song, Jiayao Teng, Fei Ren, Xianhao Gao, Yuyan Yang, Kailun |
| contents | Narrative inquiry has been one of the prominent application domains for the analysis of human experience, aiming to know more about the complexity of human society. However, researchers are often required to transform various forms of data into coherent hand-drafted narratives in storied form throughout narrative analysis, which brings an immense burden of data analysis. Participants, too, are expected to engage in member checking and presentation of these narrative products, which involves reviewing and responding to large volumes of documents. Given the dual burden and the need for more efficient and participant-friendly approaches to narrative making and representation, we made a first attempt: (i) a new paradigm is proposed, NAME, as the initial attempt to push the field of narrative inquiry. Name is able to transfer research documents into coherent story images, alleviating the cognitive burden of interpreting extensive text-based materials during member checking for both researchers and participants. (ii) We develop an actor location and shape module to facilitate plausible image generation. (iii) We have designed a set of robust evaluation metrics comprising three key dimensions to objectively measure the perceptual quality and narrative consistency of generated characters. Our approach consistently demonstrates state-of-the-art performance across different data partitioning schemes. Remarkably, while the baseline relies on the full 100% of the available data, our method requires only 0.96% yet still reduces the FID score from 195 to 152. Under identical data volumes, our method delivers substantial improvements: for the 70:30 split, the FID score decreases from 175 to 152, and for the 95:5 split, it is nearly halved from 96 to 49. Furthermore, the proposed model achieves a score of 3.62 on the newly introduced metric, surpassing the baseline score of 2.66. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction Wu, Runtong Song, Jiayao Teng, Fei Ren, Xianhao Gao, Yuyan Yang, Kailun Computer Vision and Pattern Recognition Human-Computer Interaction Narrative inquiry has been one of the prominent application domains for the analysis of human experience, aiming to know more about the complexity of human society. However, researchers are often required to transform various forms of data into coherent hand-drafted narratives in storied form throughout narrative analysis, which brings an immense burden of data analysis. Participants, too, are expected to engage in member checking and presentation of these narrative products, which involves reviewing and responding to large volumes of documents. Given the dual burden and the need for more efficient and participant-friendly approaches to narrative making and representation, we made a first attempt: (i) a new paradigm is proposed, NAME, as the initial attempt to push the field of narrative inquiry. Name is able to transfer research documents into coherent story images, alleviating the cognitive burden of interpreting extensive text-based materials during member checking for both researchers and participants. (ii) We develop an actor location and shape module to facilitate plausible image generation. (iii) We have designed a set of robust evaluation metrics comprising three key dimensions to objectively measure the perceptual quality and narrative consistency of generated characters. Our approach consistently demonstrates state-of-the-art performance across different data partitioning schemes. Remarkably, while the baseline relies on the full 100% of the available data, our method requires only 0.96% yet still reduces the FID score from 195 to 152. Under identical data volumes, our method delivers substantial improvements: for the 70:30 split, the FID score decreases from 175 to 152, and for the 95:5 split, it is nearly halved from 96 to 49. Furthermore, the proposed model achieves a score of 3.62 on the newly introduced metric, surpassing the baseline score of 2.66. |
| title | Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.00381 |