Summary of Object-conditioned Bag Of Instances For Few-shot Personalized Instance Recognition, by Umberto Michieli et al.
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
by Umberto Michieli, Jijoong Moon, Daehyun Kim, Mete Ozay
First submitted to arxiv on: 1 Apr 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 The paper addresses a crucial challenge in vision systems, namely, localizing and identifying specific instances of objects (e.g., my dog) from limited data. Despite deep networks’ impressive performance on standard benchmarks, they struggle to capture within-class variability, representing different instances rather than object categories. To tackle this issue, the authors propose an Object-conditioned Bag of Instances (OBoI) framework based on multi-order statistics of extracted features. By extending generic object detection models to search and identify personal instances in OBoI’s metric space without backpropagation, the approach achieves superior accuracy in distinguishing different instances. The results demonstrate a 12% relative gain over the state-of-the-art, with an impressive 77.1% personal object recognition accuracy for 18 personal instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a camera that can recognize and locate specific objects, like your dog or friend, from just a few pictures. Right now, computer vision systems are great at identifying general categories of things, but they struggle to tell apart individual instances. For example, it’s easy to spot dogs in general, but much harder to identify “my fluffy golden retriever” or “John’s new puppy.” This paper introduces a new approach called OBoI that can help solve this problem. By analyzing features and patterns in images, OBoI can learn to recognize specific instances of objects with high accuracy. The results are promising, showing that OBoI can correctly identify personal objects more than 12% better than existing methods. |
Keywords
» Artificial intelligence » Backpropagation » Object detection
