Xijie Cheng

TL;DR: Wang et al. as mentioned in this paper proposed a 3D convolutional neural multikernel network to capture hierarchical features for predicting crop yield, which can provide better prediction performance than the competitive methods.

TL;DR: A noise probabilistic model for learning the label noise based on the relationship between the input images, noisy labels, and true labels is developed and demonstrates its superiority over state-of-the-art methods in visual performance and classification accuracy.

TL;DR: Wang et al. as discussed by the authors proposed a novel deep learning architecture for crop yield prediction, which combines 3D convolutional and recurrent neural networks to exploit the complementarity of spatial, spectral and temporal information from multi-spectral and multi-temporal remotely sensed imagery.

TL;DR: Wang et al. as discussed by the authors developed a multilevel LC contextual (MLCC) framework that can adaptively integrate the effective global context with the local context for LC classification, and the proposed MLCC has superior capability in capturing contextual features and thus outperforms the existing methods.

TL;DR: This paper proposes a feature and label noise model (FLNM) to model the noisy label distribution in the training dataset and uses a multitask deep learning framework (MDLF) to integrate the FLNM into the training process of CNN.