Summary of Co-occurring Of Object Detection and Identification Towards Unlabeled Object Discovery, by Binay Kumar Singh et al.
Co-Occurring of Object Detection and Identification towards unlabeled object discovery
by Binay Kumar Singh, Niels Da Vitoria Lobo
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel deep learning approach for identifying co-occurring objects with base objects in multilabel object categories is proposed, enabling accurate detection of objects in images. The pipeline consists of two stages: first, bounding boxes and labels are detected using a deep learning model, followed by co-occurrence matrix analysis to identify association rules and frequent patterns between base classes and their co-occurring classes. Experimental results on Pascal VOC and MS-COCO datasets demonstrate the effectiveness of the proposed approach. Furthermore, the work is extended by considering unlabeled objects and occlusion scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to find objects that are together in images using deep learning. It’s like recognizing friends who often hang out with each other. The method works by first detecting all the things in an image and their names, then looking at how often certain objects appear together. This helps us understand what objects are likely to be found together, which is important for many applications. |
Keywords
* Artificial intelligence * Deep learning
