Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection

Author:

Feng Mingtao1,Liu Kendong1,Zhang Liang1,Yu Hongshan2,Wang Yaonan2,Mian Ajmal3

Affiliation:

1. Xidian University

2. Hunan University

3. The University of Western Australia

Funder

Australian Research Council

Publisher

IEEE

Reference59 articles.

1. An empirical study of example forgetting during deep neural network learning;toneva;International Conference on Learning Representations,0

2. Learning From Noisy Labels by Regularized Estimation of Annotator Confusion

3. Exploit and Replace: An Asymmetrical Two-Stream Architecture for Versatile Light Field Saliency Detection

4. Saliency detection via depth-induced cellular automata on light field;piao;IEEE Transactions on Image Processing,2019

5. Saliency detection via depth-induced cellular automata on light field;piao;IEEE Transactions on Image Processing,2019

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3. Mutual Information Regularization for Weakly-Supervised RGB-D Salient Object Detection;IEEE Transactions on Circuits and Systems for Video Technology;2024-01

4. An improved neural network-based saliency detection scheme for light field images;Multimedia Tools and Applications;2023-12-12

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