Summary of Understanding the Dependence Of Perception Model Competency on Regions in An Image, by Sara Pohland and Claire Tomlin
Understanding the Dependence of Perception Model Competency on Regions in an Image
by Sara Pohland, Claire Tomlin
First submitted to arxiv on: 15 Jul 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 The paper proposes five novel methods to identify regions in an input image that contribute to the incompetence of a deep neural network (DNN)-based perception model. The goal is to enable decision-making systems to reason about the model’s competency and respond appropriately when it’s incompetent. The methods, including image cropping, segment masking, pixel perturbation, competency gradients, and reconstruction loss, are assessed for their ability to identify unfamiliar objects, recognize regions associated with unseen classes, and detect unexplored areas. The results show that competency gradients and reconstruction loss methods demonstrate great promise in identifying low model competency regions, particularly when the model encounters unfamiliar aspects of the image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to understand why a perception model is incompetent, so a decision-making system can make smart choices without human intervention. It presents five new methods to find parts of an image that cause the model to be unsure or confused. These methods are tested for their ability to identify objects it doesn’t know, recognize regions it hasn’t seen before, and detect areas it hasn’t explored. The study finds that two methods stand out for accurately identifying areas where the model is uncertain. |
Keywords
» Artificial intelligence » Neural network
