Summary of Pandas: Prototype-based Novel Class Discovery and Detection, by Tyler L. Hayes et al.
PANDAS: Prototype-based Novel Class Discovery and Detection
by Tyler L. Hayes, César R. de Souza, Namil Kim, Jiwon Kim, Riccardo Volpi, Diane Larlus
First submitted to arxiv on: 27 Feb 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 This paper proposes a novel approach to object detection, PANDAS (Prototype-based ANomaly Detection And Segmentation), which enables detectors trained on a fixed set of classes to adapt to new classes encountered in real-world scenarios. By discovering clusters representing novel classes from unlabeled data and representing both old and new classes with prototypes, PANDAS can automatically enrich the detector’s repertoire to detect newly discovered classes alongside the original ones. The method is computationally efficient and performs favorably against state-of-the-art approaches on VOC 2012 and COCO-to-LVIS benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PANDAS is a way for object detectors to learn about new things they haven’t seen before. Right now, detectors are only good at recognizing specific types of objects, but this can be a problem when they’re used in real-life situations where new objects or classes might show up. PANDAS helps solve this problem by finding groups of new objects and teaching the detector to recognize them too. It’s like giving the detector a new vocabulary! The researchers tested PANDAS on some popular datasets and found that it works well and is efficient, outperforming other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Object detection
