Summary of Augmented Commonsense Knowledge For Remote Object Grounding, by Bahram Mohammadi et al.
Augmented Commonsense Knowledge for Remote Object Grounding
by Bahram Mohammadi, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, Javen Qinfeng Shi
First submitted to arxiv on: 3 Jun 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 paper proposes an augmented commonsense knowledge model (ACK) to improve vision-and-language navigation (VLN) in photo-realistic unseen environments, particularly for the REVERIE task. The existing methods rely on image or object features, which are insufficient for predicting actions. ACK leverages commonsense information as a spatio-temporal knowledge graph to enhance representation and decision-making. The model consists of modules that integrate visible objects, commonsense knowledge, and concept history. Experimental results show the proposed model outperforms the baseline and achieves state-of-the-art performance on the REVERIE benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers better at understanding instructions in real-life scenarios. Imagine you’re giving a robot directions, like “Bring me the blue cushion in the master bedroom”. The problem is that most current methods don’t work well with this kind of language. To fix this, researchers propose a new way to use common sense and visual information to help robots navigate and make decisions. They show that their approach works better than existing methods on a specific task. |
Keywords
* Artificial intelligence * Knowledge graph
