Summary of Short-term Object Interaction Anticipation with Disentangled Object Detection @ Ego4d Short Term Object Interaction Anticipation Challenge, by Hyunjin Cho et al.
Short-term Object Interaction Anticipation with Disentangled Object Detection @ Ego4D Short Term Object Interaction Anticipation Challenge
by Hyunjin Cho, Dong Un Kang, Se Young Chun
First submitted to arxiv on: 8 Jul 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 Our proposed method, SOIA-DOD, tackles the challenging task of short-term object interaction anticipation in egocentric video analysis. By decomposing this task into detecting active objects and classifying interactions with predicted timings, our approach achieves state-of-the-art performance on the challenge test set. We fine-tune a pre-trained YOLOv9 to detect potential active objects in the last frame of the video, then use transformer encoders to identify the most promising next active object and predict its future interaction and time-to-contact. Our method outperforms existing models in predicting next active objects and their interactions, ranking third overall in terms of top-5 mAP when including time-to-contact predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SOIA-DOD is a new way to understand what will happen in videos recorded from a person’s perspective. This is important because it helps machines anticipate future events and interactions between objects. Our method breaks this task into two parts: finding the objects that are likely to move or interact, and then predicting what those objects will do next. We use a special algorithm called YOLOv9 to find the objects, and another type of AI called transformer encoders to predict their future actions. Our approach does better than other methods at guessing what will happen next in videos. |
Keywords
» Artificial intelligence » Transformer
