Summary of Exploring Aleatoric Uncertainty in Object Detection Via Vision Foundation Models, by Peng Cui et al.
Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models
by Peng Cui, Guande He, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper proposes a novel approach to object detection by modeling and exploiting the uncertainty inherent in open-world datasets. The authors suggest estimating data uncertainty using vision foundation models, which are trained on ultra-large-scale datasets and can exhibit universal data representation. They assume a mixture-of-Gaussian structure of object features and devise Mahalanobis distance-based measures to quantify data uncertainty. The estimated uncertainty is then used for defining uncertainty-aware sample filters and adaptive regularizers to avoid over-fitting and balance easy/hard samples during training. Empirical studies verify the effectiveness of the proposed aleatoric uncertainty measure on various advanced detection models and challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in object detection, where images are messy and noisy. The authors want to figure out how to deal with this noise so that their models can work better. They use special kinds of AI models called “vision foundation models” to try to understand the messiness in the data. Then they use math to calculate how uncertain each piece of data is, kind of like calculating the uncertainty of a weather forecast. This calculation can help them get rid of bad data and make their models better at detecting objects. |
Keywords
» Artificial intelligence » Object detection
