Summary of Swiss Dino: Efficient and Versatile Vision Framework For On-device Personal Object Search, by Kirill Paramonov et al.
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search
by Kirill Paramonov, Jia-Xing Zhong, Umberto Michieli, Jijoong Moon, Mete Ozay
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 addresses a crucial task in robotic home appliances: personal object search. The goal is to localize and identify personal items on images captured by these devices, with each item referenced only by a few annotated images. The task involves distinguishing between many fine-grained classes, even when occlusions and clutter are present. State-of-the-art methods for few-shot learning are often not feasible due to resource constraints. To overcome this challenge, the authors propose Swiss DINO, a simple yet effective framework for one-shot personal object search based on the DINOv2 transformer model. Swiss DINO achieves significant improvements in segmentation and recognition accuracy compared to lightweight solutions, while reducing the footprint of backbone inference time and GPU consumption by up to 100x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses a problem where robotic home appliances include vision systems that can personalize devices on the fly. It discusses personal object search, which involves finding specific objects on images taken by these devices. The challenge is to identify many fine-grained classes even when there’s clutter or occlusions. The authors propose Swiss DINO, a new framework that uses the DINOv2 transformer model for one-shot learning. This helps the device understand scenes and find objects without needing special training. |
Keywords
» Artificial intelligence » Few shot » Inference » One shot » Transformer
