Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts
TLDR
This paper proposes an image captioning system that exploits the parallel structures between images and sentences and makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image.Citations
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Book ChapterDOI
Exploring Visual Relationship for Image Captioning
TL;DR: Zhang et al. as discussed by the authors proposed GCN-LSTM with attention mechanism to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework.
Posted Content
Exploring Visual Relationship for Image Captioning.
TL;DR: This paper introduces a new design to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework that novelly integrates both semantic and spatial object relationships into image encoder.
Journal ArticleDOI
A survey on automatic image caption generation
Shuang Bai,Shan An +1 more
TL;DR: A survey on advances in image captioning research is presented, and neural network based methods used in early work which are mainly retrieval and template based are discussed.
Journal ArticleDOI
Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition
Siyue Xie,Haifeng Hu,Yongbo Wu +2 more
TL;DR: A novel model, named Deep Attentive Multi-path Convolutional Neural Network (DAM-CNN), that can automatically locate expression-related regions in an expressional image and yield a robust image representation for FER.
Posted Content
A Survey of the Usages of Deep Learning in Natural Language Processing
TL;DR: An introduction to the field and a quick overview of deep learning architectures and methods is provided and a discussion of the current state of the art is provided along with recommendations for future research in the field.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.