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arXiv · 2504.11232

Leveraging multimodal explanatory annotations for video interpretation with Modality Specific Dataset

Elisa Ancarani·Julie Tores·Lucile Sassatelli·Rémy Sun·Hui-Yin Wu·Frédéric Precioso
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Abstract

We examine the impact of concept-informed supervision on multimodal video interpretation models using MOByGaze, a dataset containing human-annotated explanatory concepts. We introduce Concept Modality Specific Datasets (CMSDs), which consist of data subsets categorized by the modality (visual, textual, or audio) of annotated concepts. Models trained on CMSDs outperform those using traditional legacy training in both early and late fusion approaches. Notably, this approach enables late fusion models to achieve performance close to that of early fusion models. These findings underscore the importance of modality-specific annotations in developing robust, self-explainable video models and contribute to advancing interpretable multimodal learning in complex video analysis.

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Elisa Ancarani, Julie Tores, Lucile Sassatelli, Rémy Sun, Hui-Yin Wu, Frédéric Precioso. 2025-04-15. Leveraging multimodal explanatory annotations for video interpretation with Modality Specific Dataset. https://arxiv.org/abs/2504.11232

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