Summary of Multi-modal Interpretable Automatic Video Captioning, by Antoine Hanna-asaad et al.
Multi-Modal interpretable automatic video captioning
by Antoine Hanna-Asaad, Decky Aspandi, Titus Zaharia
First submitted to arxiv on: 11 Nov 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 video captioning method is a novel approach to describe video contents using natural language format, focusing on understanding scenes, actions, and events from visual and audio cues. The method employs multi-modal contrastive loss to emphasize integration of both modalities, resulting in more accurate captions. Additionally, the model uses attention mechanisms to provide interpretability into its decision-making process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to describe videos using words. It’s like trying to summarize what’s happening in a movie or TV show. The scientists are improving this by combining information from both pictures and sounds, which helps them make more accurate descriptions. They also want to understand why their computer model is making certain choices, so they added special “attention” tools that explain the thinking behind its decisions. |
Keywords
» Artificial intelligence » Attention » Contrastive loss » Multi modal
