Summary of Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-person Action Recognition, by Cagri Gungor and Adriana Kovashka
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition
by Cagri Gungor, Adriana Kovashka
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 This paper presents a multimodal framework for first-person activity recognition that addresses domain shifts by incorporating motion, audio, and appearance features. The proposed method analyzes the robustness of audio and motion features to environmental changes, utilizes audio narrations for better alignment with visual cues, and applies consistency ratings between audio and visual narratives to optimize the contribution of audio in training. The approach achieves state-of-the-art performance on the ARGO1M dataset, demonstrating effective generalization across unseen scenarios and locations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us recognize activities by using cameras and other sensors. It’s hard for machines to understand what people are doing when things change around them (like a different room or objects). The researchers developed a new way of combining information from these sensors that works better than before. They tested it on a big dataset and showed that it can recognize activities in new, unseen situations. |
Keywords
» Artificial intelligence » Activity recognition » Alignment » Generalization
