Summary of Situation Monitor: Diversity-driven Zero-shot Out-of-distribution Detection Using Budding Ensemble Architecture For Object Detection, by Qutub Syed et al.
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection
by Qutub Syed, Michael Paulitsch, Korbinian Hagn, Neslihan Kose Cihangir, Kay-Ulrich Scholl, Fabian Oboril, Gereon Hinz, Alois Knoll
First submitted to arxiv on: 5 Jun 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 A novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models, called Situation Monitor, is introduced to enhance reliability in safety-critical machine learning applications like autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases OOD performance by integrating a diversity loss into training on top of DBEA, detecting Far-OOD samples while minimizing false positives on Near-OOD samples. Additionally, the resulting DBEA model improves OOD performance and confidence score calibration, particularly regarding object intersection over union. The DBEA model achieves this with a 14% reduction in trainable parameters compared to the vanilla model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Situation Monitor is a new way to make sure machines that detect objects don’t get confused when things they’re not supposed to see show up. This is important for self-driving cars and other safety-critical applications. The new approach uses something called DBEA, which helps detect far-out-of-distribution samples and reduces false positives on near-out-of-distribution samples. This makes the machines better at detecting what’s really there and less likely to be wrong. |
Keywords
» Artificial intelligence » Machine learning » Object detection » Transformer » Zero shot
