| Challenge: | Visual storytelling is the task of generating a story paragraph that describes a given image sequence. |
| Approach: | They propose 3 evaluation metrics sets that analyze which aspects we would look for in a good story . they compare their correlation with human judgement scores on a sample of machine stories . |
| Outcome: | The proposed evaluation metrics outperform other metrics on human correlation on a sample of machine stories from state-of-the-art models. |
Similar Papers
GROOViST: A Metric for Grounding Objects in Visual Storytelling (2023.emnlp-main)
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| Challenge: | Several evaluation metrics for visual storytelling do not consider images at all . authors propose a novel evaluation tool that accounts for cross-modal dependencies and temporal misalignments . |
| Approach: | They propose a visual storytelling evaluation tool that evaluates visual grounding . they use cross-modal dependencies, temporal misalignments and human intuitions . |
| Outcome: | The proposed evaluation tool accounts for cross-modal dependencies, temporal misalignments and human intuitions on visual grounding. |
Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)
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| Challenge: | Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation. |
| Approach: | They develop a Vrank metric that repurposes human evaluation results for automatic evaluation. |
| Outcome: | The proposed model is more accurate than existing metrics and is generalizable to textual stories. |
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition (2024.findings-emnlp)
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| Challenge: | Visual storytelling is a task of generating a story for a sequence of several temporally-ordered images or video frames. |
| Approach: | They propose a method that measures story quality in terms of human likeness regarding three key aspects highlighted in previous work: visual grounding, coherence, and repetitiveness. |
| Outcome: | The proposed method improves on the foundation model LLaVA but only slightly compared to TAPM, a 50-times smaller visual storytelling model. |
Visual Coherence Loss for Coherent and Visually Grounded Story Generation (2023.findings-acl)
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| Challenge: | Existing visual storytelling models fail to generate correct referring expressions for characters, causing 60% of the generated stories to be lacking local coherence. |
| Approach: | They propose a loss function inspired by a linguistic theory of coherence for self-supervised learning for image sequence representations and a feature matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. |
| Outcome: | The proposed features and loss function are effective for generating more coherent and visually grounded stories. |
Visual Story Post-Editing (P19-1)
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| Challenge: | a dataset for human edits of machine-generated visual stories is released . it includes 14,905 human-edited versions of 2,981 machine- generated visual stories . |
| Approach: | They introduce the first dataset for human edits of machine-generated visual stories . they explore how edits may be used for the visual story post-editing task . |
| Outcome: | The proposed dataset includes 14,905 human-edited versions of 2,981 machine-generated visual stories. |
Generating Visual Stories with Grounded and Coreferent Characters (2026.tacl-1)
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| Challenge: | Experimental results show that our model generates visual stories with consistent and coreferent character mentions compared to baselines and state-of-the-art systems. |
| Approach: | They propose a character-centric approach to visual story generation that uses visual and textual character coreference chains to enrich the VIST benchmark. |
| Outcome: | The proposed model generates visual stories with consistent and coreferent character mentions compared to baselines and state-of-the-art systems. |
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling (2024.eacl-long)
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| Challenge: | Visual storytelling aims to automatically generate a coherent story based on a given image sequence. |
| Approach: | They propose a framework that represents the image sequence as a graph with objects and relations that includes human action motivation and its social interaction commonsense knowledge. |
| Outcome: | The proposed framework produces stories superior across multiple metrics in terms of visual grounding, coherence, diversity, and humanness, per both automatic and human evaluations. |
Plot and Rework: Modeling Storylines for Visual Storytelling (2021.findings-acl)
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| Challenge: | Automated visual storytelling models do not make extensive use of external knowledge and iterative generation when attempting to create stories. |
| Approach: | They propose a framework that uses an image sequence as a story graph to create a coherent story. |
| Outcome: | The proposed framework produces stories superior in diversity, coherence, and humanness . it uses plotting and reworking to improve the model's performance, the authors say . |
Stretch-VST: Getting Flexible With Visual Stories (2021.acl-demo)
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| Challenge: | Existing visual storytelling models produce stories with fixed lengths of five sentences and the fix-length stories carry limited details and provide ambiguous textual information to the readers. |
| Approach: | They propose to “stretch” visual storytelling frameworks by adding appropriate knowledge to the model to generate long stories. |
| Outcome: | The proposed framework provides better focus and detail when long stories are generated without deteriorating the quality. |
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale (2020.coling-main)
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| Challenge: | a few popular metrics are still used to evaluate language generation systems despite their known limitations. |
| Approach: | They propose to use automatic metrics to evaluate language generation systems . they show that they prefer system outputs to human-authored texts . |
| Outcome: | The proposed metrics are insensitive to correct translations of rare words and can yield high scores when given a single sentence as system output for the entire test set. |