3D convolutional neural network (3D CNN) captures spatial and temporal information on data such as video sequences. However, due to the convolution pooling mechanism, loss that occurs seems unavoidable. To improve visual explanations classification in CNN, we propose two approaches; (i) aggregate layer-wise global local (global–local) discrete gradient using trained 3DResNext network, (ii) impl...

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially boundary and highly textured regions. To tackle this challenge, work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining local...

Recently, Transformer-based models have shown promising results in automatic speech recognition (ASR), outperforming based on recurrent neural networks (RNNs) and convolutional (CNNs). However, directly applying a Transformer to the ASR task does not exploit correlation among frames effectively, leaving model trapped sub-optimal solution. To this end, we propose local attention for that combine...

Computerized tomography (CT) scan images are widely used in automatic lung cancer detection and classification. The nodules’ texture distribution throughout the CT volume can vary significantly, accurate identification consideration of discriminative information this greatly help classification process. Deep stacks recurrent convolutional operations cannot entirely represent such variations, es...