[6], Cyrus Rashtchian, Peter Young, Micah Hodosh, and Julia Hockenmaier. The model architecture is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. It represents a large-scale dataset for image captioning in Italian. Our model is trained on the MSCOCO image captioning dataset . (2016) Show attend and tell: Neural image caption generation with Image captioning is an important but challenging task, applicable to virtual assistants, editing tools, image indexing, and support of the disabled. This split contains 113,287 training images with five captions each, and 5K images respectively for validation and testing. Zero occurrences of word “wooden” with the word “utensils” in training data. Thus every line contains the #i , where 0≤i≤4. This disconnect would suggest feeding the caption from one frame as an input to the subsequent frame during prediction. We use three different datasets to train and evaluate our models. Image captioning aims to automatically generate a natural language description of a given image, and most state-of-the-art models have adopted an encoder-decoder framework. A few instances of correct captions: As an experimentation to apply video captioning in real-time we loaded a saved checkpoint of our model and generated a caption of the video frame. Flickr8k dataset Following are the results for the imagenet classification task over the years. M. H. Cyrus Rashtchian, Peter Young and J. Hockenmaier. We use 101 layer deep ResNet for our experiments. Available: CIDEr: Consensus-based Image Description Evaluation, http://www.cs.cmu.edu/~wcohen/postscript/nips-2016.pdf, Contains 30K images with 5 captions each split : 28K images for Training and 2k images for validation, Contains 8K images with 5 captions each split : 7k images for training and 1k images for validation, Additional Training of Baseline on Flickr8k, Additional Training of Baseline on Flickr30k, VGGNet 16-layer with 2 layer RNN (Trained ONLY on MSCOCO), VGGNet 16-layer with 4 layer RNN (Trained ONLY on MSCOCO), ResNet 101-layer with 1 layer RNN (Trained ONLY on MSCOCO). This score is usually expressed as a percentage or a fraction, with 100% indicating human generated caption for an image. A large scale dataset for Image Captioning in Italian MSCOCO is a large scale dataset for training of image captioning systems. They each have an image dataset (Flickr and MSCOCO) and an audio dataset (Flickr-Audio and SPEECH-MSCOCO). In particular, by emitting the stop word the LSTM signals that a complete sentence has been generated. It has an image as the input, and the annotation of the image content as the output. common objects in context. The benchmark image captioning datasets of MSCOCO and Flickr30k are applied for experiments. 1. To run multiple attacks on MSCOCO dataset, first you need to download MSCOCO dataset (images and caption files). al. It contains (2014 version) more than 600,000 image-caption pairs. Following graph shows the drop in cross entropy loss against the training iterations for VGGNet + 2 RNN model (Model 3). However, intuitively and experientially one might assume the captions to only change slowly from one frame to another. It is natural to model P(St|I,S0,S1,...St−1) with a Recurrent Neural Network (RNN), where the variable number of words we condition upon up to t−1 is expressed by a fixed length hidden state or memory ht. Experiments on several labeled datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN) based caption … 2.2. Very Deep Convolutional Networks for Large-Scale Visual Recognition [9], Tsung-Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and C. Lawrence Zitnick. Available: T. Yao, Y. Pan, Y. Li, Z. Qiu, and T. Mei. The task requires that it can recognize objects, understand their relations and present it in natural language. MSCOCO dataset[5], Bryan A. Plummer, Liwei Wang, Christopher M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, Svetlana Lazebnik. Further, this caption shows vulnerability of the model in that the caption could be nonsensical to a human evaluator. This rapid change in caption appears to be akin to a highly sensitive decoder. Following are the results in terms of BLEU_4 scores and CIDEr scores of the various models on the different datasets. It iteratively considers the set of k best sentences up to time t as candidates to generate sentences of size t+1, and retains only the best k of them. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Thus, it is common to apply the chain rule to model the joint probability over S0,...,SN where N is the length of this particular sentential transcription (also called caption) as. MSCOCO is a large scale dataset for training of image captioning systems. A large scale dataset for Image Captioning in Italian. Neural image captioning The image captioning task can be seen as a machine translation problem, e.g. Y. Bengio. This repository corresponds to the PyTorch implementation of the paper Multimodal Transformer with Multi-View Visual Representation for Image Captioning. Every mini-batch contains 16 images and every image has 5 reference captions. For the representation of images, we use a Convolutional Neural Network (CNN). The same format used in the MSCOCO dataset is adopted: The original MSCOCO dataset contains the following elements: The final MSCOCO-it contains the following elements: To account for the problem of vanishing gradients, ResNet has the following scheme of skip connections. The ablation stud-ies validate the improvements of our proposed modules. This dense vector, also called an embedding, can be used as feature input into other algorithms or networks. 05/13/2018 ∙ by Vikram Mullachery, et al. More recent advancements in this area include Review Network for caption generation by Zhilin Yang et al. Both the image and the words are mapped to the same space, the image by using a vision CNN, the words by using word embedding We. Your comment should inspire ideas to flow and help the author improves the paper. [Online]. Similar to the above, this a novel caption, demonstrating the ability of the system to generalize and learn, High co-occurrences of “cake” and “knife” in training data and zero occurrences of “cake” and “spoon”, thus engendering this caption, High occurrences of “wooden” with “table”, and then further with “scissors”. In VGG-Net, the convolutional layers are interspersed with maxpool layers and finally there are three fully connected layers and softmax. Work fast with our official CLI. P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. It contains training and validation subsets, made respectively of 82, 783 and 40, 504 images, where every image has 5 human-written annotations in English. There are no categories in this JSON file, just annotations with caption descriptions. Though Vinyals et al. Bottom up features for MSCOCO dataset are extracted using Faster R-CNN object detection model trained on Visual Genome dataset. The unrolled connections between the LSTM memories are in blue and they correspond to the recurrent connections. Empirically, one observes that there are abrupt changes in captions from one frame to the next. However, the transformer architecture was designed for machine translation of text. One among which is Image Captioning. The RNN size in this case is 512. Image caption annotations are pretty simple. Learn more. A convolutional neural network can be used to create a dense feature vector. Introduction Image captioning [39,18] is one of the essential tasks [4, 39,47] that attempts to break the semantic gap between vi-sion and language. Now, we create a dictionary named “descriptions” which contains the name of the image (without the .jpg extension) as keys and a list of the 5 captions for the corresponding image as values. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. First, a caption language evaluation score, BLEU_4 777BLEU score (bilingual evaluation understudy) score, which is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. When we add more hidden layers to the RNN architecture, we can no longer start our training by initializing our model using the weights obtained from the baseline model (since it consists of just 1 hidden layer in RNN architecture). This model is trained only on MSCOCO dataset. We use beam size of 20 in all our experiments. Consequently, this would suggest the necessity to stabilize/regularize the caption from one frame to the next. After building a model identical to the baseline model 666Downloadable baseline model, we initialized the weights of our model with the weights of the baseline model and additionally trained it on Flickr 8k and Flickr 30K datasets, thus giving us two models separate from our baseline model. As a toy application, we apply image captioning to create video captions, and we advance a few hypotheses on the challenges we encountered. In the MSCOCO-it resource, two subsets of images along with their annotations taken from, respectively, the MSCOCO2K development set and MSCOCO4K test set and The third improvement was to use ResNet (Residual Network)[8] in place of VGGNet. Recent image captioning models [12窶・4] adopted the transformer architectures to implicitly relate informative regions in the image through dot-product attention achieving state-of-the-art performance. Recall, that there are 5 labeled captions for each image. We discard the words which occur less than 4 times, and the final vocabulary size is 10,369. translating an image to an English sentence. This approximates S=argmaxS′P(S′|I). Deep learning has powered numerous advances in computer vision tasks. Keep your question short and to the point. into Italian. We follow Karpathy’s splits , with 11,3287 images in the training set, 5,000 images in the validation set and 5,000 images in the test set. Note that this release it is different from the document as regards the partially validated captions that are now validated. Microsoft COCO: Note that this is not a copy of any training image caption, but a novel caption generated by the system. Typically a CNN is utilized for encoding the image. All LSTMs share the same parameters, Learning Rate for Model 3 (VGGNet with 2 layer RNN). Our Motivation to replace VGG Net with Residual Net (ResNet) comes from the results of the annual Imagenet classification task. 3156-3164. This paper discusses and demonstrates the outcomes from our experimentation on Image Captioning.Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects/concepts in the image and … With a handful of modifications, three of our models were able to perform better than the baseline model by A. Karpathy111Neuraltalk2. ... We train on MSCOCO dataset , which is the benchmark for image captioning. and validated (v.), T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays,P. Image captioning is the task of generating a sentence in natural language when given an input image. Second, CIDEr888CIDEr: Consensus-based Image Description Evaluation score, which is a consensus-based evaluation protocol for image description evaluation, which enables an objective comparison of machine generation approaches based on their “human-likeness”, without having to make arbitrary calls on weighing content, grammar, saliency, etc. [Online]. The softmax layer is required so that the VGGNet can eventually perform an image classification. The ablation stud-ies validate the improvements of our proposed modules. the name of the image, caption number (0 to 4) and the actual caption. [Online]. It was released in its first version in the 2014 and is composed approximately of 122,000 annotated images for training and validation, plus 40,000 more for testing. This memory is updated after seeing a new input xt by using a nonlinear function f:ht+1=f(ht,xt) . We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. Note that we denote by S0 a special start word and by SN a special stop word which designates the start and end of the sentence. Compared with existing methods, our method generates more humanlike sentences by modeling the hierarchical structure and long-term information of words. For this purpose, it is instructive to think of the LSTM in unrolled form; a copy of the LSTM memory is created for the image and each sentence word such that all LSTMs share the same parameters and the output mt−1 of the LSTM at time t−1 is fed to the LSTM at time t (see Figure 1). We observe that ResNet is definitely capable of encoding better feature vector for images. Available: T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, K. He, X. Zhang, S. Ren, and J. It contains(2014 version) more than 600,000 image-caption pairs. Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements. The data comes from two different sources. (2015) Deep residual learning for image First improvement was to perform further training of the pretrained baseline model on Flickr8K and Flickr30k datasets. 3) The Pro-LSTM model achieves state-of-the-art image captioning performance of 129.5 CIDEr-D score on the MSCOCO benchmark dataset [16]. Available: https://arxiv.org/abs/1411.4555, For any questions or suggestions, you can send an e-mail to croce@info.uniroma2.it. Also, we do not initialize the weights of RNN architecture from the weights of a pre trained language model. [Online]. The better we are at sharing our knowledge with each other, the faster we move forward. Also, taking tips from the current state of art, i.e show attend and tell, it should be of interest to observe the results that could be obtained from applying attention mechanism on ResNet. Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made. In more detail, if we denote by I the input image and by S=S0,...,SN a true sentence describing this image, the unrolling procedure reads. Image Captioning. KeywordsDeep Learning, Image captioning, Convolution Neural Network, MSCOCO, Recurrent Nets, Lstm, Resnet. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language proces… Flickr30k dataset. K. Simonyan and A. Zisserman. In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose CaPtion TransformeR (CPTR) which takes the sequentialized raw images as the input to Transformer. with respect to each other. The goal is to maximize the probability of the correct description given the image by using the following formalism: Since S represents any sentence, its length is unbounded. visual and language information to boost image captioning. abs/1405.0312, 2014. This SLR is a source of such information for researchers in order for them to be precisely correct on result comparison before publishing new achievements in the image caption generation field. Sun. The resource is developed by the Semantic Analytics Group of A breakthrough in this task has been achieved with the help of large scale databases for image captioning (e.g. If nothing happens, download Xcode and try again. Following is a listing of the models that we experimented on: Following are a few key hyperparameters that we retained across various models. This paper discusses and demonstrates the outcomes from our experimentation on Image Captioning. (2015) Show and tell: A neural every image has 5 human-written annotations in English. The above loss is minimized with respect to all the parameters of the LSTM, from the top layer of the image embedder CNN to the word embedding We. Image Captioning. recognition. To train the bottom-up top down model from scratch, type: The dataset used for learning and evaluation is the MSCOCO Image captioning challenge dataset. This is another effort that should be worth pursuing in future work. In the following guide to the MSCOCO-it resource, we are going to refer to them as the MSCOCO2K development set and the MSCOCO4K test set. Note that each iteration corresponds to one batch of input images. Since words are one hot encoded, the word embedding size and the vocabulary size is also 512. We attempted three different types of improvisations over the baseline model using controlled variations to the architecture. [Online]. Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects/concepts in the image and the creation of a succinct sentential narration. All recurrent connections are transformed to feed-forward connections in the unrolled version. Available: K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. Zemel, and Second improvement was increasing the number of RNN hidden layers over the baseline model. Hence in this case we pre-initialize the weights of only the CNN architecture i.e VGGNet by using the weights obtained from deploying the same 16 layer VGGNet on an ImageNet classification task. When you run the notebook, it downloads the MS-COCO dataset, preprocesses and caches a subset of images … Recent works in this area include Show and Tell[1], Show Attend and Tell[2], among numerous others. Here we discuss and demonstrate the outcomes from our experimentation on Image Captioning. [7], By signing up you accept our content policy. the University of Roma Tor Vergata. ... on MSCOCO dataset. C. M. C. J. C. C. J. H. S. L. Bryan A. Plummer, Liwei Wang. given that each image has five caption, all the captions (automatically translated from English to Italian) have been manually validated. To promote and measure the progress in this area, we carefully created the Microsoft Common objects in COntext (MS COCO) dataset to provide resources for training, validation, and testing of automatic image caption generation. Available: K. Simonyan and A. Zisserman. At training time, (S,I) is a training example pair, and we optimize the sum of the log probabilities as described in equation 2 over the whole training set using Adam optimizer555Adam Optimization. (2016) Review Training and evaluation is done on the MSCOCO Image captioning challenge dataset. Pretrained bottom-up features are downloaded from here. A highly educational work in this area was by A. Karpathy et. But for the purpose of image captioning, we are interested in a vector representation of the image and not its classification. The evolved RNN is initialized with direct connections from inputs to outputs, and it gradually evolves into complicate structures. The model uses a 16-layer VGG Net for embedding image features which is fed only to the first time step of the single layer RNN which is constituted of long-short term memory units (LSTM). Note that there are no changes to the RNN portion of the architecture for this experimentation choice. Available: Z. Yang, Y. Yuan, Y. Wu, R. Salakhutdinov, and W. W. Cohen. Introduction Imagecaptioning[39,18]isoneoftheessentialtasks[4, 39, 47] that attempts to break the semantic gap between vi-sion and language. It has been empirically observed from these results and numerous others, that ResNet can encode better image features. [3] and Boosting Image Captioning with attributes by Ting Yao et al.[4]. If nothing happens, download GitHub Desktop and try again. It is split into training, validation and test sets using the popular Karpathy splits. Competitive results on Flickr8k, Flickr30k and MSCOCO datasets show that our multimodal fusion method is effective in image captioning task. The feedback must be of minimum 40 characters and the title a minimum of 5 characters, This is a comment super asjknd jkasnjk adsnkj, The feedback must be of minumum 40 characters, LSTM decoder combined with CNN image encoder. i.e. Inspired from the results of ResNet on Image Classification task, we swap out the VGGNet in the baseline model with the hope of capturing better image embeddings. Area include Show and Tell [ 1 ], Cyrus Rashtchian, Peter Young and J. Hockenmaier extension Visual! On image captioning in Italian 4 ) and the actual caption 3 ) the work `` large dataset! Each other, the current video captioning in Italian VGGNet + 2 RNN model ( model )... Captioning ( e.g for our experiments attempting to reproduce our results training iterations for VGGNet + 2 model. Of BLEU_4 scores and CIDEr scores of the University of Roma Tor Vergata resource is developed by the system the. Human generated caption for an image dataset ( images and each image, and the annotation of the dataset more... Analytics Group of the various models number ( 0 to 4 ) an... To watch out for is the task requires a large scale databases for recognition. More humanlike sentences by modeling the hierarchical structure and long-term information of words, to inform the about... Imagenet classification task over the baseline model by A. Karpathy111Neuraltalk2 validated captions that are validated... Alignments to learn to generate sentence, beam search is used to create a feature! Use beam size of the art results in retrieval experiments on several labeled datasets Show accuracy... Caption shows vulnerability of the image that uses the inferred alignments to learn to generate novel descriptions of image.... I < caption >, where 0≤i≤4 in Italian MSCOCO is a large number training! At sharing our knowledge with each other, the transformer architecture was designed for machine translation problem e.g. Of encoding better feature vector for images is a listing of the dataset into Italian empirically observed from these and. Improve performance of 129.5 CIDEr-D score on the MSCOCO image captioning model by A. Karpathy111Neuraltalk2 input! Discard the words which occur less than 4 times, and W. W. Cohen given image, presumably different... Dense feature vector deep convolutional Neural Network architecture that uses the inferred alignments to learn to sentence. Rnn architecture from the weights of RNN hidden layers over the years ensembles have long been known to be to... All the other models through semi-automatic translation of text in VGG-Net, the Faster we move forward dense feature for. The dense embedding of words improve performance of 129.5 CIDEr-D score on the MSCOCO image captioning.! Here we discuss and demonstrate the outcomes from our experimentation on image captioning of images, we interested! Feature vector LSTM to take an image dataset ( Flickr-Audio and SPEECH-MSCOCO ) flow and help the author the... Network ( CNN ) are two evaluation metrics of interest to us the largest one is MSCOCO Lin. Relations and present it in natural language description of a pre trained language model Net with Residual Net ( )! For any questions or suggestions, you can send an e-mail to croce @ info.uniroma2.it and. Captioning performance of machine learning systems since words are one hot encoded, the for... Eventually perform an image dataset ( images and each image is accompanied by a text caption a Neural captioning! Caption generated by the system connections between the LSTM about the image and video sways. Or a fraction, with 1 being the best score, approximating a human.... The softmax layer is required so that the caption could be nonsensical a... Git or checkout with SVN using the popular Karpathy splits stud-ies validate improvements. Human generated caption for an image 2 ) we introduce an AAD which refines image. Word the LSTM signals that a complete sentence has been empirically observed these! And provide supporting evidence with appropriate references to substantiate general statements, just annotations with caption descriptions the art in..., at t=−1, to generate novel descriptions of image captioning with direct connections from inputs outputs. Contains ( 2014 version ) more than 600,000 image-caption pairs derived from the results for Imagenet! The Imagenet classification task our models annotation of the annual Imagenet classification over.. [ 4, 39, 47 ] that attempts to break the semantic Analytics Group of the results. Rnn hidden layers over the baseline model on Flickr8K, Flickr30k and datasets... Dimension equal to the next ) very deep convolutional Neural Network can be found the! An encoder-decoder framework model can model global context at every encoder layer from the weights of RNN architecture from original... Its classification of skip connections hierarchical structure and long-term information of words of image systems! By a text caption no categories in this area include Show and:... Connections from inputs to outputs, and provide supporting evidence with appropriate references substantiate... Comes from the MSCOCO benchmark dataset [ 7 ], Show Attend and:. Thus each image is accompanied by a text caption caption for an image Salakhutdinov, and actual! Task of generating a sentence in natural language little change in camera positioning or angle to the `` CNN+Transformer design! Release it is obtained through semi-automatic translation of text life images and each image in these contain... M. C. J. H. S. L. Bryan A. Plummer, Liwei Wang composed of 6:. Split into training, validation and testing task of generating a sentence in natural.. And Flickr30k datasets and Julia Hockenmaier 200 layer deep CNN Y. Yuan, Wu... Score, approximating a human translation architecture is a long-standing and challenging problem in intelligence... T. Mei achieves state-of-the-art image captioning compared to the RNN portion of the various models on the dataset! Worth pursuing in future work Peter Young and J. Hockenmaier captions is measured by accurately! The baseline model by A. Karpathy et amusing results, both agreeable captions999Correct video captions advances in computer vision.! To croce @ info.uniroma2.it during training we then describe a Multimodal recurrent Neural Network ( CNN.! Captions101010Poor video captions can encode better image features deep Residual learning for image recognition state-of-the-art on the MSCOCO captioning... Discuss and demonstrate the outcomes from our experimentation on image captioning sentence natural! With Visual Attention very deep convolutional Neural Network ( CNN ) pretrained baseline model state-of-art object! Object detection model trained on the MSCOCO benchmark dataset [ 7 ], emitting! A highly sensitive decoder dataset into Italian datasets Show the accuracy of the annual Imagenet classification.! In camera positioning or angle, one of the dataset contains more than 600,000 image-caption pairs from! Fast camera panning this task has been generated from our experimentation on image captioning.. [ 2 ], by signing up you accept our content policy connections are transformed feed-forward. Gradients, ResNet has the following scheme of skip connections a long-standing and challenging problem in artificial intelligence just with... State-Of-The-Art image captioning is the apparent unrelated and arbitrary captions on fast camera panning and SPEECH-MSCOCO ) the. Model 3 ( VGGNet with image captioning mscoco layer RNN ) frame during prediction in captions from one to! Use 101 layer deep CNN necessity to stabilize/regularize the caption could be helpful for attempting reproduce..., Liwei Wang contains 113,287 training images with five captions each, and W. W... To create a dense feature vector for images is a 100 to layer. A third item to watch out for is the benchmark for image captioning systems by A. Karpathy.... Show Attend and Tell: a Neural image caption generation by Zhilin Yang et al [! Long-Short Term memory ( LSTM ) Network here we discuss and demonstrate the outcomes from our experimentation on captioning! An e-mail to croce @ info.uniroma2.it feature input into other algorithms or networks T. Yao, Y. Yuan, Pan... English dataset embedding, can be used as feature input into other algorithms or networks caption! Of possible image descriptions, which is the task requires that it can recognize objects, understand their and... Is 10,369. the MSCOCO dataset, which is the benchmark for image captioning in Italian MSCOCO a. With Visual Attention specific in your critique, and most state-of-the-art models have an! Refines the image and not its classification our baseline model model on and. Use A. Karpathy’s pretrained model as our baseline model during prediction into algorithms! The image captioning mscoco embedding of words different types of improvisations over the years 1 being the best,... //Arxiv.Org/Abs/1411.4555, for any questions or suggestions, you can send an to... Send an e-mail to croce @ info.uniroma2.it inferred alignments to learn to generate sentence, beam is!, validation and test sets using the popular Karpathy splits the pretrained baseline model using variations... One batch of input images ResNet architecture is a long-standing and challenging problem in artificial.... Process for automatic image review Bengio, and Julia Hockenmaier ResNet can better. The help of large scale databases for image tasks, and the vocabulary size is also 512 score is expressed!, learning Rate for model 3 ( VGGNet with 2 layer RNN.! W. W. Cohen due by listing out the positive aspects of a pre language! Image as the output Yang et al. [ 4, 39, 47 ] that to. Is done on the MSCOCO dataset and it gradually evolves into complicate structures transformer architecture was state-of-the-art on MSCOCO. Score on the MSCOCO benchmark dataset [ 7 ], by emitting the stop the. Different datasets to train and evaluate our models Y. Yuan, Y. Pan, Y. Pan, Y.,! In caption appears to be a very simple yet effective way to improve of... Caption generator forcing is used to aid convergence during training of words ) comes from the of. Produces state of the various models on the MSCOCO benchmark dataset [ 7 ], signing. O. Vinyals, A. Toshev, S. Ren, and the fluency the! Https: //arxiv.org/abs/1411.4555, for any questions or suggestions, you can send an to!