Mr. Yu Qiao Profile

Yu Qiao

at Shenzhen Institutes of Advanced Technology

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Publications (2)

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Proceedings Article | 23 May 2023 Paper

Uncertainty-estimation with normalized logits for out-of-distribution detection

Mouxiao Huang, Yu Qiao

Proceedings Volume 12645, 1264526 (2023) https://doi.org/10.1117/12.2681144

KEYWORDS: Machine learning, Data modeling, Neural networks, Statistical modeling, Reliability

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Out-of-Distribution (OOD) detection is critical for preventing deep learning models from making incorrect predictions, especially in safety-critical applications such as medical diagnosis and autonomous driving. However, neural networks often suffer from overconfidence, making high confidence predictions for OOD data that are never seen during training and may be irrelevant to training data. Determining the reliability of the prediction is still a difficult and challenging task. To address this challenge, we propose a new method called Uncertainty-Estimation with Normalized Logits (UE-NL) for robust learning and OOD detection. The method has three main benefits: (1) Neural networks with UE-NL treat every In Distribution (ID) sample equally by predicting the uncertainty score of input data and the uncertainty is added into SoftMax function to adjust the learning strength of easy and hard samples during training phase, making the model learn robustly and accurately. (2) UE-NL enforces a constant vector norm on the logits to decouple the effect of the increasing output’s norm from optimization process, which causes the overconfidence issue to some extent. (3) UE-NL provides a new metric, the magnitude of uncertainty score, to detect OOD data. Experiments demonstrate that UE-NL outperforms existing methods on common OOD benchmarks and is more robust to noisy ID data that may be misjudged as OOD data by other methods.

SPIE Journal Paper | 15 February 2017

Improving scale invariant feature transform with local color contrastive descriptor for image classification

Sheng Guo, Weilin Huang, Yu Qiao

JEI, Vol. 26, Issue 01, 013015, (February 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.1.013015

KEYWORDS: Image classification, Databases, Computer programming, Visualization, Optical filters, RGB color model, Image filtering, Binary data, Image segmentation, Facial recognition systems

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Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.

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