Summary of Quantifying the Reliability Of Predictions in Detection Transformers: Object-level Calibration and Image-level Uncertainty, by Young-jin Park and Carson Sobolewski and Navid Azizan
Quantifying the Reliability of Predictions in Detection Transformers: Object-Level Calibration and Image-Level Uncertainty
by Young-Jin Park, Carson Sobolewski, Navid Azizan
First submitted to arxiv on: 2 Dec 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 DETECTION TRANSFORMER (DETR) has emerged as a promising architecture for object detection, offering an end-to-end prediction pipeline. The paper presents empirical evidence highlighting how different predictions within the same image play distinct roles, resulting in varying reliability levels across those predictions. While multiple predictions are often made for a single object, most often one such prediction is well-calibrated, and the others are poorly calibrated. Identifying a reliable subset of DETR’s predictions is crucial for accurately assessing the reliability of the model at both object and image levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DETECTION TRANSFORMER (DETR) is a new way to find objects in pictures. Right now, DETR makes lots of guesses about what’s in a picture, but not all those guesses are correct. The people who made DETR wondered: can we trust all the guesses? They looked at how good each guess was and found that some guesses were really reliable while others weren’t. This is important because it helps us figure out which guesses to trust when using DETR. |
Keywords
» Artificial intelligence » Object detection » Transformer
