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arXiv · 2609.39051

TSMD: Temporal-Stream Modality Dropout for Robust Video Highlight Detection

Bo-Yuan Cheng·Kuan-Yu Chen·Po-Han Huang·Jeng-Lin Li·Jian-Jiun Ding
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Abstract

Existing multimodal video highlight detectors typically assume that visual, audio, and textual streams are continuously available. In practice, however, inputs may suffer from localized frame missingness or complete-stream outage. We formulate this robustness challenge along two dimensions: temporal missingness, where frames are missing independently in each modality, and stream-level missingness, where one modality is unavailable throughout a video. Moreover, we find that the mean squared error (MSE) loss is misaligned with both the evaluation metrics and the peak-driven nature of highlights. Therefore, we propose Temporal-Stream Modality Dropout (TSMD), which combines structured missingness simulation with a joint objective comprising pointwise MSE, per-video Pearson correlation, and peak-oriented RankNet loss terms. TSMD has three variants: temporal, stream-level, and mixed dropout. On the MoSu and Mr. HiSum datasets, TSMD-Temporal improves mAP@15 by 7.06 and 3.41 points over TripleSumm under 50% independent temporal removal, whereas TSMD-Stream performs the best under complete-stream removal. TSMD-Mix retains most of these complementary benefits and ranks the best or the second-best across the evaluated temporal and stream-level conditions.

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Bo-Yuan Cheng, Kuan-Yu Chen, Po-Han Huang, Jeng-Lin Li, Jian-Jiun Ding. 2026-09-30. TSMD: Temporal-Stream Modality Dropout for Robust Video Highlight Detection. https://arxiv.org/abs/2609.39051

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