Summary of Open-set Object Detection: Towards Unified Problem Formulation and Benchmarking, by Hejer Ammar et al.
Open-set object detection: towards unified problem formulation and benchmarking
by Hejer Ammar, Nikita Kiselov, Guillaume Lapouge, Romaric Audigier
First submitted to arxiv on: 8 Nov 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 The proposed paper addresses the issue of unknown object detection in real-world applications such as autonomous driving, where accurate detection and handling of classes not seen during training are crucial. Despite various approaches, inconsistencies exist among datasets, metrics, and scenarios used, and there is a lack of clear definition for unknown objects, hindering meaningful evaluation. To address this, the paper introduces two benchmarks: VOC-COCO evaluation and OpenImagesRoad benchmark with hierarchical object definition and new evaluation metrics. The study also exploits self-supervised Vision Transformers to improve pseudo-labeling-based OpenSet Object Detection (OSOD) through OW-DETR++. State-of-the-art methods are evaluated on the proposed benchmarks, providing a clear problem definition, consistent evaluations, and new insights into OSOD strategy effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on improving unknown object detection in real-world applications. It’s about making sure that self-driving cars can recognize objects they haven’t seen before. Right now, different methods use different datasets, metrics, and scenarios, which makes it hard to compare results. The researchers introduce two new benchmarks to solve this problem. They also show how a new type of AI model called Vision Transformers can be used to improve unknown object detection. The study provides a clear definition of the problem, consistent ways to evaluate methods, and new insights into what works well for detecting unknown objects. |
Keywords
» Artificial intelligence » Object detection » Self supervised
