Object-Conditioned Bag of Instances

Table of Links

Abstract and 1. Introduction

2. Few-Shot Personalized Instance Recognition

3. Object-Conditioned Bag of Instances

4. Experimental Results

5. Conclusion

6. References

3. OBJECT-CONDITIONED BAG OF INSTANCES

We propose a lightweight module that can be integrated into any object detection network. Our solution is based on three key components: (i) an object detection network, with (ii) a multi-order statistical augmentation of embeddings for (iii) instance-level recognition via an OBoI. Next, we outline how we construct our OBoI to personalize object detectors pretrained on To on the server side (see Fig. 2).

The overall pipeline can be thought of as an Object-conditioned Bag of Instances (OBoI) since generic categorylevel output is converted to specific personal-level output via conditional nearest prototype selection. Our setup and method are fully compatible with the key requirement of continually learning new instances over time [6, 5, 16, 24, 25, 26]; whenever a user presents a new instance to be recognized, we can include new instance-level prototypes in the OBoI at any time with no accuracy degradation with respect to the case where all instances are available from the beginning of the adaptation process.