Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

Mundane

Residual CRNN and Its Application to Handwritten Digit String Recognitione applied to most network architectures. In this paper, we embrace these observations and present a new string recognition model named Residual Convolutional Recurrent Neural Network (Residual CRNN, or Res-CRNN) based on CRNN and residual connections. We add residual connections to convolutional lay

可轉(zhuǎn)變

JAUNT

慌張

Preserve

Dense Image Captioning Based on Precise Feature Extractiong has emerged, which realizes the full understanding of the image by localizing and describing multiple salient regions covering the image. Despite there are state-of-the-art approaches encouraging progress, the ability to position and to describe the target area correspondingly is not enough as we

CONE

Improve Image Captioning by Self-attentiony determined by visual features as well as the hidden states of Recurrent Neural Network (RNN), while the interaction of visual features was not modelled. In this paper, we introduce the self-attention into the current image captioning framework to leverage the nonlocal correlation among visual feat

Tinea-Capitis

Dual-Path Recurrent Network for Image Super-Resolutioners blindly leads to overwhelming parameters and high computational complexities. Besides, the conventional feed-forward architectures can hardly fully exploit the mutual dependencies between low- and high-resolution images. Motivated by these observations, we first propose a novel architecture by t

撫慰

Attention-Based Image Captioning Using DenseNet Featureshe whole scene to generate image captions. Such a mechanism often fails to get the information of salient objects and cannot generate semantically correct captions. We consider an attention mechanism that can focus on relevant parts of the image to generate fine-grained description of that image. We

時(shí)代

High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention correlated information of LF, most of the previous methods have to stack several convolutional layers to improve the feature representation and result in heavy computation and large model sizes. In this paper, we propose channel-wise and SAI-wise attention modules to enhance the feature representat