Summary of Objectrelator: Enabling Cross-view Object Relation Understanding in Ego-centric and Exo-centric Videos, by Yuqian Fu et al.
ObjectRelator: Enabling Cross-View Object Relation Understanding in Ego-Centric and Exo-Centric Videos
by Yuqian Fu, Runze Wang, Yanwei Fu, Danda Pani Paudel, Xuanjing Huang, Luc Van Gool
First submitted to arxiv on: 28 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 A novel method, ObjectRelator, is introduced to tackle the emerging Ego-Exo Object Correspondence task in computer vision, which aims to map objects across ego-centric and exo-centric views. The approach features two new modules: Multimodal Condition Fusion (MCFuse) and SSL-based Cross-View Object Alignment (XObjAlign). MCFuse fuses language and visual conditions to enhance target object localization, while XObjAlign enforces consistency in object representations across views through self-supervised alignment. State-of-the-art performance is achieved on Ego2Exo and Exo2Ego tasks with minimal additional parameters. This work provides a foundation for future research in comprehensive cross-view object relation understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to match objects between different viewpoints is developed, called ObjectRelator. It has two special parts: one that combines language and visual information to find objects, and another that makes sure the same object looks similar when viewed from different angles. This helps the method be more accurate at finding objects in different situations. The approach does very well on tasks that require matching objects between viewpoints, even with a small amount of extra information. This research is important for understanding how objects relate to each other and their surroundings. |
Keywords
» Artificial intelligence » Alignment » Self supervised
