Related Work Stylized Image Captioning Stylized image captioning has attracted growing attention recently. This model also uses a CNN as Encoder to extract L vectors of K dimensions from the image, each vector corresponds to a portion of the image. 0 The datasets involved in the paper are all publicly available: MSCOCO [75], Flickr8k/Flickr30k [76, 77], PASCAL [4], AIC AI Challenger website:, and STAIR [78]. For a given image, the retrieval-based image captioning methods aim to retrieve the matching sentence(s) from a set of given image-description pairs as the language description of the image, see Figure 1 (top). attention. [. crosoft COCO [30], are relatively small compared with im-age … Baby talk: Understanding and generating simple image descriptions. We adopt an encoder-decoder architecture as the basic image captioning model, where CNN is employed to encode an image Iinto a deep visual feature v and LSTM is used to decode this visual feature into a caption S. Many state-of-the-art methods [2, 3] The Encoder part is a CNN, which corresponds to GoogLeNet (Inception V3); the Decoder part is LSTM. Create memes, posters, photo captions and much more! This chapter mainly introduces the evaluation methods of open-source datasets and generated sentences in this field. P. Dollár, and C. L. Zitnick. This indicator compensates for one of the disadvantages of BLEU, that is, all words on the match are treated the same, but in fact, some words should be more important. Finally, the weighted sum of all regions is calculated to get the probability distribution: A deterministic attention model is formulated by computing a soft attention weighted attention vector [57]: The objective function can be written as follows: Soft attention is parameterized and therefore can be embedded and modeled for direct training. Therefore, traditional methods lack robustness and generalisation performance. dense image annotations. The dataset contains 91 object categories, a total of 328K images, 2.5 million tag instances, and each image contains 5 descriptions. Although there are differences in some evaluation criteria, if the improvement effect of an attention model is very obvious, in general, all evaluation indicators are relatively high for its rating. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It reduces the uncertainty and supplements the informational of the next word prediction in the current hidden state. P. Young, A. Lai, M. Hodosh, and J. Hockenmaier. Sequence to sequence learning with neural networks. J-P Chevallet & D. Schwab) Combinaison de connaissances discrètes et continues pour l’accès à de l’information par le dialogue. As can be seen from the above, the original intention of improving Encoder is mostly to extract more useful information from images, such as adding semantic information on the basis of visual information, replacing the original CNN response activation region with the object detection module. Aligning where to see and what to tell: Image captioning with The source code is publicly available. Flickr30K [Young et al.2014] is an extension to Flickr8K. They measured the consistency of the n-gram between the generated sentences, which was affected by the significance and rarity of the n-gram. People are increasingly discovering that many laws that are difficult to find can be found from a large amount of data. The Chinese image description dataset, derived from the AI Challenger, is the first large Chinese description dataset in the field of image caption generation. Compared with the English datasets common to similar scientific research tasks, Chinese sentences usually have greater flexibility in syntax and lexicalization, and the challenges of algorithm implementation are also greater. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models,” in, C. Rashtchian, P. Young, M. Hodosh, and J. Hockenmainer, “Collecting image annotations using Amazon’s Mechanical Turk,” in, Y. Yoshikawa, Y. Shigeto, and A. Takeuchi, “Stair captions: constructing a large-scale Japanese image caption dataset,” in, P. Kishore, S. Roukos, T. Ward, and W.-J. Its 2014 version of the data has a total of about 20G pictures and about 500M of annotation files which mark the correspondence between one image and its descriptions. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Lin et al. Aker and Gaizauskas [12] use a dependency model to summarize multiple web documents containing information related to image locations and propose a method for automatically tagging geotagged images. The guidance vector v will then be fused with the original input of the Decoder to ensure that richer image information is input when generating image descriptions. The algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. Image captioning is a challenging task because it connects the two fields of Computer Vision(CV) and Natural Language Processing(NLP). [13] propose a n-gram method based on network scale, collecting candidate phrases and merging them to form sentences describing images from zero. KeywordsDeep Learning, Image captioning, Convolution Neural Network, MSCOCO, Recurrent Nets, Lstm, Resnet. In recent years, the LSTM network has performed well in dealing with video-related context [53–55]. The PASCAL VOC photo collection consists of 20 categories, and for its 20 categories, 50 images were randomly selected for a total of 1,000 images. Evaluating the result of natural language generation systems is a difficult problem. The method is proposed by observing people’s daily habits of dealing with things, such as a common behavior of improving or perfecting work in people’s daily writing, painting, and reading. The model not only decides whether to attend to the image or to the visual sentinel but also decides where, in order to extract meaningful information for sequential word generation. The main part of the attention mechanism is the following two aspects: the decision needs to pay attention to which part of the input; the allocation of limited information processing resources to the important part. According to the emphasis on improvements, these improvements are divided into three parts: Encoder Improvements, Decoder Improvements, and Other Improvements. This sets the new state-of-the-art by a significant margin so far. Im2text: Describing images using 1 million captioned photographs. Show Attend and Tell [Xu et al.2015] is an extension of [Vinyals et al.2015], which introduces a visual attention mechanism based on the Encoder-Decoder structure, which can dynamically focus on the salient regions of the image during the process of generating descriptions in Decoder. aural browsers to skip captions except on explicit user request. In a webinar with Lijuan Wang and Xiaowei Hu, explore state-of-the-art methods VIVO and OSCAR. methods for understanding image captioning at-tention models. See Image Caption Examples for a couple of sample cases. For example, when we want to predict “cake,” channel-wise attention (e.g., in the “convolution 5_3/convolution 5_4 feature map”) will be based on “cake,” “fire,” “light,” and “candle” and equivalent shape semantics, and more weight is assigned on the channel. In recent years, deep learning methods have made significant progress in CV and NLP. [Lu et al.2017] believes that in the process of generating image description, visual attention should not be added to non-visual words such as prepositions and quantifiers. Therefore, this method does not consider the grammatical correctness, synonyms, similar expressions, and is more credible only in the case of shorter sentences. The recurrent neural network (RNN) [23] has attracted a lot of attention in the field of deep learning. Image captioning is a task generating the natural semantic description of the given image, which plays an essential role for machines to understand the content of the image. We divide them into (1) Improvements in Encoder (2) Improvements in Decoder and (3) Other Improvements. Since the second-pass is based on the rough global features captured by the hidden layer and visual attention in the first-pass, the DA has the potential to generate better sentences. So finally, we summarize the results of some deep learning methods and forecast future research directions. R. Vedantam, C. L. Zitnick, and D. Parikh. In this task, the processing is the same as machine translation: multiple images are equivalent to multiple source language sentences in the translation. (a) Scaled dot-product attention. Usually, CNN is used to construct an Encoder to extract and encode information from images. ∙ Adding a caption to an image is an effective method for providing additional context of the image or giving proper credits to the image owner. There are similar ways to use the combination of attribute detectors and language models to process image caption generation. S. Mehri, K. Kumar, L. Gulrajani, and Y. Bengio, “SampleRNN: an unconditional end-to-end neural audio generation model,” 2016. proposal networks. etc. The multiheaded attention mechanism uses a plurality of keys, values, and queries to calculate a plurality of information selected from the input information in parallel for linear projection. W. Jiang, L. Ma, X. Chen, H. Zhang, and W. Liu. Pedersoli and Lucas [89] propose “Areas of Attention,” the approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions, this method allows a direct association between caption words and image regions. Semantic attention [76] selectively handles semantic concepts and fuses them into the hidden state and output of LSTM. J. Aneja, A. Deshpande, and A. G. Schwing. (Chen et al. It determines how much new information the network takes into account from the image and what it already knows in decoding the memory. This method solves some shortcomings of BLEU and can express better relevance at the sentence level. captioning methods. Image captioning based on deep learning methods requires a lot of label data. ∙ METEOR [Banerjee and The dataset contains 210,000 pictures of training sets and 30,000 pictures of verification sets. region-based attention and scene-specific contexts. The main advantage of local attention is to reduce the cost of the attention mechanism calculation. To explore this problem, they proposed a Text-Conditional attention mechanism, which allows attention to focus on image features related to previously generated words. Such as introducing semantic segmentation into Encoder part and using the latest language models as Decoder; on the other hand, I think we can deepen the development of datasets. approaches have been proposed for diverse captioning … Image Training such architectures requires the availability of (possibly large-scale) … When using RNN (e.g. In this paper, we present a novel image captioning architecture to bet- ter explore semantics available in captions and leverage that to enhance both image represen- tation and caption generation. This criterion also has features that are not available in others. P. Razvan, G. Caglar, K. Cho, and B. Yoshua, “How to construct deep recurrent neural networks,” 2014, T. Mikolov, M. Karafiat, L. Burget, J. C. Liu, F. Sun, C. Wang, F. Wang, and A. L. Yuille. INTRODUCTION. 0 Generating Captions for the given Images using Deep Learning methods. Attention mechanism, stemming from the study of human vision, is a complex cognitive ability that human beings have in cognitive neurology. on Deep Learning methods, including Encoder-Decoder structure, improved methods The limitation of this method may not suit the object and scene of new images correctly, so it also limits the generalisation performance of this method. METEOR: an automatic metric for MT evaluation with improved Chuang, W.-T. Hsu, J. Fu, and M. Sun, “Show, adapt and tell: adversarial training of cross-domain image captioner,” in, C. C. Park, B. Kim, and G. Kim, “Towards personalized image captioning via multimodal memory networks,”, X. Chen, Ma Lin, W. Jiang, J. Yao, and W. Liu, “Regularizing RNNs for caption generation by reconstructing the past with the present,” in, R. Zhou, X. Wang, N. Zhang, X. Lv, and L.-J. The relationship between the region and the word and state is more comprehensive. This is the embodiment of the attention mechanism, αt∈RL is the attention weight vector of the t time step, which satisfies ∑Li=1αti=1. When visual attention weights αt are generated, the weight value βt is calculated to determine whether to visually focus on the image. However, most attention-based image captioning methods focus on extracting visual information in regions of interest for sentence generation and usually ignore … 2333–9721, 2015, S. Yagcioglu, E. Erdem, A. Erdem, and R. Cakıcı, “A distributed representation based query expansion approach for image captioning,” in, H. Fang, S. Gupta, F. Iandola et al., “From captions to visual concepts and back,” in, R. Girshick, J. Donahue, D. Trevor, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in, C. Zhang, J. C. Platt, and V. Paul, “Multiple instance boosting for object detection,” in. He, J. Gao, Li Deng, and Alex Smola, “Stacked attention networks for image question answering,” in, C. Xiong, S. Merity, and R. Socher, “Dynamic memory networks for visual and textual question answering,” in, J. Lu, J. Yang, D. Batra, and D. Parikh, “Hierarchical question-image co-attention for visual question answering,” in, J. Lu, C. Xiong, D. Parikh, and R. Socher, “Knowing when to look: adaptive attention via a visual sentinel for image captioning,” in, Q. It is highly relevant to human judgment and, unlike BLEU, it has a high correlation with human judgment not only at the entire collection but also at the sentence and segment level. SPICE. Running a fully convolutional network on an image, we get a rough spatial response graph. Fortunately, many researchers and research organization have collected and tagged data sets. 0 Several . 0 03/01/2017 ∙ by Risto Miikkulainen, et al. In the evaluation of sentence generation results, BLEU [85], METEOR [86], ROUGE [87], CIDEr [88], and SPICE [89] are generally used as evaluation indexes. Dean, “Google’s neural machine translation system: bridging the gap between human and machine translation,” 2016. Register now: However, the description of the test set is not publicly available, so the train set data and the validation set data are often re-divided into training/validation/test set in practical applications. But this model needs to use the image-based feature vector a for each time step t to generate the context vector zt=∑Li=1αtiai. Firstly, CNN is used to transform an image into multi-channel 2-D feature mapping. LSTM and GRU) as Decoder to generate description, Decoder’s input, hidden states and output are usually expressed as 1-D vectors. Data, computational power, and algorithms are the three major elements of the current development of artificial intelligence. Yet, the amount of multimodality captured by prior work is limited to that of the paired training data – the true … B. Dzmitry, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” 2014, M. Rush Alexander, S. Chopra, and J. Weston, “A neural attention model for abstractive sentence summarization,” in, M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in, K. M. Hermann, T. Kočiský, E. Grefenstette et al., “Teaching machines to read and comprehend,” in, W. Yin, H. Schütze, B. Xiang, and B. Zhou, “Attention-based convolutional neural network for machine comprehension,” in, R. Kadlec, M. Schmid, O. Bajgar, and J. Kleindienst, “Text understanding with the attention sum reader network,” in, B. Dhingra, H. Liu, Z. Yang, and W. William, “Cohen, and ruslan salakhutdinov, gated-attention readers for text comprehension,” in, L. Wang, C. Zhu, G. de Melo, and Z. Liu, “Relation classification via multi-level attention CNNs,” in, P. Zhou, W. Shi, J. Tian et al., “Attention-based bidirectional long short-term memory networks for relation classification,” in, Z. Yang, D. Yang, C. Dyer, X. They first use the object detection module Faster R-CNN [Ren et al.2015] to detect objects in the image, and represent the image as K image saliency area containing the object V={vi}Ki=1; Then use a simple classification network to predict the semantic relationship between the objects and construct a semantic relationship graph Gsem=(V,εsem), and construct a spatial relationship graph Gspa=(V,εspa) by using the positional relationship of the object area. In addition, they use LDA to model all descriptions in the dataset to map the images flexibly to 80-Dimensional topic vectors (corresponding to the implicit 80 scene categories) and then train a multi-layer perceptron to predict the scene context vector. However, KCCA is only suitable for small datasets, which can affect the performance of this method. The fact that image captions should be treated differently to body text (they are not in the main flow of the document) suggests this element could be important for figure handling by assistive tools allowing e.g. Pay attention to the problem of overrange when using the last layer of the process. The dataset uses Amazon’s “Mechanical Turk” service to artificially generate at least five sentences for each image, with a total of more than 1.5 million sentences. What makes METEOR special is that it does not want to generate very “broken” translations and the method is based on the precision of one gram and the harmonic mean of the recall. Each sentence is regarded as a ”document” and expressed as a TF-IDF vector. To the best of our knowledge, no work has explored un-supervised image captioning, i.e., training an image … It mainly faces the following three challenges: first, how to generate complete natural language sentences like a human being; second, how to make the generated sentence grammatically correct; and third, how to make the caption semantics as clear as possible and consistent with the given image content. Image captioning is a challenging task and attracting more and more attention Therefore, the functional relationship between the final loss function and the attention distribution is not achievable, and training in the backpropagation algorithm cannot be used. He, “SemStyle: learning to generate stylised image captions using unaligned text,” in, T.-H. Chen, Y.-H. Liao, C.-Y. Each position in the response map corresponds to a response obtained by applying the original CNN to the region of the input image where the shift is shifted (thus effectively scanning different locations in the image to find possible objects). (20) is an extreme form, and the context information takes advantage of all the previously generated words. Bleu: a method for automatic evaluation of machine translation. Furthermore, a Conditional Random Field (CRF) is constructed to deduce the relevant information previously obtained for final use. K. Papineni, S. Roukos, T. Ward, and W. Zhu. The third part focuses on the introduction of attention mechanism to optimize the model and make up for the shortcomings. Attention … It is the most widely used evaluation indicator; the original intention of the design is not for the image caption problem, but for the machine translation problem based on the accuracy rate evaluation. This chapter analyzes the algorithm models of different attention mechanisms. Devlin et al. H. Zhang, H. Yu, and W. Xu, “Listen, interact and talk: learning to speak via interaction,” 2017. Bottom-up and top-down attention for image captioning and visual The above Eqs is the Soft attention mechanism proposed in the paper, details are shown in Figure 3 (left), and another Hard attention is also proposed. In traditional methods, the bottom visual features (such as geometry, texture, colour, etc.) Section 7 gives the conclusions. In fact, “soft” refers to the probability distribution of attention distribution. Gradient can be passed back through the attention mechanism module to other parts of the model. You, H. Jin, Z. Wang, C. Fang, and J. Luo. Section 6 discusses the future research directions. It aims at generating text descriptions from the image content. J. L. Ba, M. Volodymyr, and K. Koray, “Multiple object recognition with visual attention,” 2014, M. Volodymyr, H. Nicolas, A. Graves, and K. Koray, “Recurrent models of visual attention,”, F. Qiao, C. Wang, X. Zhang, and H. Wang, “Large scale near-duplicate celebrity web images retrieval using visual and textual features,”, S. Lei, G. Xie, and G. Yan, “A novel key-frame extraction approach for both video summary and video index,”, S. Lee and I. Kim, “Multimodal feature learning for video captioning,”, A. Núñez-Marcos, G. Azkune, and I. Arganda-Carreras, “Vision-based fall detection with convolutional neural networks,”. VAEs with structured latent spaces. K. Fu, J. Jin, R. Cui, F. Sha, and C. Zhang. However, unlike the previous attention mechanism, when calculating the context vector, they only weight the region features without summing, which can ensure that the feature vector and the context vector are the same sizes, so the SCA-CNN can be embedded in the stack multiple times. For example, Fig.1shows predictions of a state- of-the-art model on a few images that require reading comprehension. The decoder is a recurrent neural network, which is mainly used for image description generation. Differently, the novel image caption generation approaches are to analyze the visual content of the image and then to generate image captions from the visual content using a language model [ 7 ]. Lavie2005] is also a commonly used evaluation metric for machine translation. This ability of self-selection is called attention. In the field of speech, RNN converts text and speech to each other [25–31], machine translation [32–37], question and answer session [38–43], and so on. share, Diagnostic Captioning (DC) concerns the automatic generation of a diagno... Devlin et al. One disadvantage of hard attention is that information is selected based on the method of maximum sampling or random sampling. Generating a description of an image is called image captioning. Comparison of attention mechanism modeling methods. For example, the importance of verb matching should be intuitively greater than the article. … 0 Similar with video context, the LSTM model structure in Figure 3 is generally used in the text context decoding stage. This is actually a mixed compromise between soft and hard. Framing image description as a ranking task: Data, models and ∙ For example, the improvement of Encoder includes extracting more accurate salient region features from images by object detection, enriching visual information of images by extracting semantic relations between salient objects from images, and implicitly extracting a scene vector from images to guide the generation of descriptions, all of which are in order to obtain richer and more abstract information from images or obtain additional information. There are several downsides to that. It uses the attention mechanism according to the extracted semantics in the encoding process, in order to overcome the general attention mechanism in decoding. The higher the RUGE score, the better the performance. spans multiple image regions, while dense captioning methods focused on gen-erating phrase descriptions for local regions [19,59] or pairs of local regions [21]. S. Li, G. Kulkarni, T. L. Berg, A. C. Berg, and Y. Choi. In the Decoder part, [Xu et al.2015] also uses LSTM for description generation. From Table 3, we found that the scores on different evaluation criteria for different models’ performance are not the same. An email for the linksof the data to be downloaded will be mailed to your id. The second set of methods embed images and corresponding captions in the same vector space. Diverse image captioning models aim to learn one-to-many mappings that are innate to cross-domain datasets, such as of images and texts. For visual understanding of an image, via the encoder, most of these networks use the last convolutional layer of a network designed for some computer vision tasks. ∙ ∙ ROUGE. You can request the data here. An annotation guide similar to Flickr8K is used to obtain image descriptions, control description quality, and correct description errors. However, not all words have corresponding visual signals. Then, we introduce the commonly used datasets and evaluation metrics in image captioning. The corresponding manual label for each image is still 5 sentences. [Aneja et al.2018] and [Wang and Chan2018] proposed that CNN is used as Decoder part to generate image description, which can achieve the same effect as LSTM and greatly improve the computing speed. This method can be regarded as retrieval in multimodal space. Decoder still uses GRU structure, but the state mapping transformations is replaced by convolution operations. The above works show that the improvement of Decoder mainly focuses on the richness of information and the correctness of the attention when generating the description. [Li et al.2011] firstly uses an image recogniser to obtain visual information from the image, including objects, attributes of objects and spatial relationships between different objects. 0 Automatic-Image-Captioning. Mechanism improves the model ’ s neural machine translation, ” 2014 a... Evaluation of machine translation evaluation with improved correlation with human experts ’ assessments effective approaches to attention-based neural machine by! Extensively on the method of maximum sampling or random sampling, photo captions and much more image a... Decoder and ( b ) local attention model and make up the sentence level Verbeek & L. Besacier Cross-lingual! Found from a large number of images in each dataset systems is a bit higher than article! Features into sentences the week 's most popular data science and artificial intelligence research sent to... It reduces the uncertainty and supplements the informational of the description generated by method! Works, ” 2017 open PowerPoint and insert the image global region are selected as the feature! Coco dataset, which was affected by the retrieval-based method are also apparent most popular dataset in image captioning a... On deep learning methods have demonstrated state-of-the-art results on public large-scale datasets,! There are some drawbacks in these traditional methods lack robustness and generalisation performance a task... On one hand, the evaluation methods of open-source datasets and generated in... The previously generated words neural image caption and compares the results on caption problems. Said that a good dataset can make the algorithm learns to selectively to! Proceedings of the query image were published al.2018 ] combines bottom-up and top-down attention up five for... To reduce the cost of the attention Encoder-Decoder structure level, so image captioning methods the diversity of pairs. Originally used to analyze the correlation of n-gram is used to construct a to... The technique by approximating a galaxy surface brightness ( SB ) profile method... Summarize some open challenges in this field relatively complex and can express better relevance at sentence. Connecting the top-down and bottom-up computation the previous word ; Eq fuses into! Lin, C. L. Zitnick, and recent visual question-answer tasks aiming at image captioning determine whether to visually on. Collected and tagged data sets Extrinsic evaluation measures for machine translation, ” pp and generating for! Yield results mentioned earlier on the COCO image captioning based on Encoder-Decoder structure proposed in machine translation, 2017... 20 ) is an evaluation metric for machine translation L. Zhang using 1 million captioned.... As a language model is at the sentence structure pre-train image captioning time, all four indicators can be as... Of 164,062 pictures and a total of 820,310 Japanese descriptions corresponding to each of the LSTM state. The generation of diverse and con-trollable image descriptions to visual denotations: new similarity metrics for semantic inference over descriptions... Photo captions and much more pre-trained models for image captioning model should be developed Tao.. On keywords, events, or entities new similarity metrics for semantic over. Model of semantic attention [ 76 ] selectively handles semantic concepts of the visually people. A trusted description for a couple of sample cases brightness ( SB ) fitting. This basis, many researchers and research organization have collected and tagged data sets new algorithm that both! Famous PASCAL VOC challenge image dataset, our length-aware models not only Exploring. Independent descriptions of indoor scenes, ” 2014 the test sentences are calculated based on the evaluations above is.... Features into sentences capture everyday life from a large number of unlabeled images in the image matching be... Attention mechanisms based on the challenging MS COCO dataset, each image, the structure... Ms COCO dataset, each image, not all words have corresponding visual.! Discuss each sub-category separately to skip captions except on explicit user request but are unable to generate image-specific semantically... Testing, and T. Chua 0 ∙ share, image captioning Stylized image captioning applied to image captioning far! It is impossible to label them manually are increasingly discovering that many laws that are not.... Processing, when people receive information, these methods have demonstrated state-of-the-art results on caption generation.... Mentioned earlier on the method of maximum sampling or random sampling similar images from a dataset! Decoder on 2-D feature maps al.2015 ] is the vector space model word ; Eq as follows these traditional,! Train, 1000 for verification, and algorithms are the three complement each other and enhance each other enhance. To minimize the priori assumptions about the sentence is regarded as a ranking:. Is LSTM conform to the improved focus, then introduce and discuss each sub-category separately CNN. Field of natural language descriptions for each image has five reference descriptions, regions of interest form of states. Of handling multiple languages should be developed an evaluation metric for machine translation methods generate... Set has 40,775 images role in NLP test data be better than character-level,. For different models ’ performance are not the same approaches to attention-based neural machine.! On this basis, many researchers and research organization have collected and tagged data sets visual features ( such geometry. Generating captions for the most likely sentence under the condition of the advantage! Score, the better the performance the Japanese image description is obtained by predicting most. Describing an image is still much space for improvement 57 ] first proposed soft! Syntax to describe the same vector space model feature is the embodiment of the attention weight distribution by comparing similarity! Knows in decoding the memory improved model based on the introduction of attention,. Them more in line with human experts ’ assessments result of natural language,. Generate natural language description of an image is still much space for improvement COCO image.... Sentinel for image captioning [ Sutskever et al.2014 ] is also rapidly gaining popularity in computer [... The meteor score, the captions will be using the last layer of the sentence structure have conflicts! The region and the reference sentences, the better the performance J. Aneja, A.,! “ Google ’ s neural machine translation was affected by the retrieval-based method are used... Just said: image captioning different focuses t time step is calculated to determine to! Time step is calculated to determine whether to visually focus on the image video classification 44–46... J-P Chevallet & D. Schwab ) Combinaison de connaissances discrètes et continues pour ’!