Summary of On the Inherent Robustness Of One-stage Object Detection Against Out-of-distribution Data, by Aitor Martinez-seras et al.
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
by Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad, Alain Andres, Pablo Garcia-Bringas
First submitted to arxiv on: 7 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 This paper investigates the robustness of one-stage object detectors in handling out-of-distribution (OoD) data. The authors propose a novel detection algorithm that leverages features extracted by the model to identify unknown objects without requiring retraining. Unlike previous approaches, this method exploits supervised dimensionality reduction and high-resolution feature maps for unsupervised unknown object detection. The authors analyze the Pareto trade-off between known and unknown object detection performance under different configurations and inference confidence thresholds. They also compare their proposed algorithm with logits-based post-hoc OoD methods and possible fusion strategies, demonstrating improved performance when combined with state-of-the-art OoD approaches on the Unknown Object Detection benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well object detection models work when they’re shown new or unusual data. The authors created a new way to detect unknown objects in images using features from the model itself, without needing to retrain it. This method uses techniques like dimensionality reduction and high-resolution maps to find unknown objects. They tested different settings and compared their approach with others, showing that combining their method with state-of-the-art approaches improves performance. |
Keywords
» Artificial intelligence » Dimensionality reduction » Inference » Logits » Object detection » Supervised » Unsupervised
