Summary of Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality, by Sishuo Chen et al.
Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
by Sishuo Chen, Lei Li, Shuhuai Ren, Rundong Gao, Yuanxin Liu, Xiaohan Bi, Xu Sun, Lu Hou
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Missing-Resistant framework, MR-VPC, aims to develop a more realistic and practical approach to video paragraph captioning (VPC) by leveraging all available auxiliary modalities, including speech and event boundaries. The current models are limited by the assumption of constant availability of a single modality, which is not feasible in real-world scenarios. To address this limitation, MR-VPC integrates video, speech, and event boundary inputs in a unified manner using the Multimodal VPC (MVPC) architecture. Additionally, to ensure robustness against incomplete data, the authors introduce DropAM, a data augmentation strategy that randomly omits auxiliary inputs, paired with DistillAM, a regularization target that distills knowledge from teacher models trained on modality-complete data. The framework is evaluated on YouCook2 and ActivityNet Captions datasets, demonstrating superior performance in both modality-complete and modality-missing test scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to create detailed descriptions for long videos using various sources like speech and event timing. This approach, called Missing-Resistant VPC, can work even when some of these sources are missing or unavailable. They created a special architecture that combines all the available information from video, speech, and event boundaries to generate captions. To make sure their model works well with incomplete data, they introduced two new techniques: one that randomly removes some of the sources and another that helps the model learn from more complete data. The results show that their approach performs better than current methods in both situations where all the information is available and when it’s not. |
Keywords
» Artificial intelligence » Data augmentation » Regularization
