Deming Zhai's research while affiliated with Harbin Institute of Technology and other places
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Publications (3)
Supervised deep learning has achieved tremendous success in many computer vision tasks, which however is prone to overfit noisy labels. To mitigate the undesirable influence of noisy labels, robust loss functions offer a feasible approach to achieve noise-tolerant learning. In this work, we systematically study the problem of noise-tolerant learnin...
Depth images play an important role and are prevalently used in many computer vision and computational imaging tasks. However, due to the limitation of active sensing technology, the captured depth images in practice usually suffer from low resolution and noise, which prevents its further applications. To remedy this problem, in this paper, we firs...
Citations
... In this study, we apply the RMDNet framework to several state-of-the-art loss function methods, including generalized cross-entropy (GCE) [25], symmetric cross-entropy (SCE) [23], NCE AUL is NCE combined with AUL, NCE AGCE is NCE combined with AGCE [46]. In addition, we have the traditional cross-entropy loss (CE). ...
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... In the equation, f(x, y) represents the pixel value at (x, y), and w(i, j) represents the convolution kernel. The Laplacian operator, often used in image processing, plays a crucial role in extracting gradient information from images [28,29]. It accentuates abrupt changes in pixel values, making it particularly useful in highlighting boundaries and fine details, such as edges of clouds or broken clouds. ...
