Liu, “Sentence level recurrent topic model: letting topics speak for themselves,” 2016, S.-H. Chen and C.-C. Ho, “A hybrid statistical/RNN approach to prosody synthesis for Taiwanese TTS,” in, W. Hinoshita, T. Ogata, H. Kozima, H. Kanda, T. Takahashi, and H. G. Okuno, “Emergence of evolutionary interaction with voice and motion between two robots using RNN Intelligent robots and systems,” in, Z. Wu, X. Wang, Y.-G. Jiang, H. Ye, and X. Xue, “Modeling spatial-temporal clues in a hybrid deep learning framework for video classification,” in, X. Yang, P. Molchanov, and J. Kautz, “Multilayer and multimodal fusion of deep neural networks for video classification,” in, Z. Wu, X. Wang, Y.-G. Jiang, H. Ye, and X. Xue, “Multi-stream multi-class fusion of deep networks for video classification,” in, S. Ilya, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in. Lu et al. Generating a caption for a given image is a challenging problem in the deep learning domain. The source code is publicly available. Once deployed, you can test the model from the command line. The image description task is similar to machine translation, and its evaluation method extends from machine translation to form its own unique evaluation criteria. 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. Secondly, since the feature map depends on its underlying feature extraction, it is natural to apply attention in multiple layers; this allows obtaining visual attention on multiple semantic abstractions. are far from applications to describing images that we encounter. It samples the hidden state of the input by probability, rather than the hidden state of the entire encoder. This app is ideal if you want to get more likes and add nice captions to make your posts on social networks more interesting. 3. G. Klein, K. Yoon, Y. Deng, and A. M. Rush, “OpenNMT: open-source toolkit for neural machine translation,” 2017. They measured the consistency of the n-gram between the generated sentences, which was affected by the significance and rarity of the n-gram. Object detection is also performed on images. The datasets involved in the paper are all publicly available: MSCOCO [75], Flickr8k/Flickr30k [76, 77], PASCAL [4], AIC AI Challenger website: https://challenger.ai/dataset/caption, and STAIR [78]. It measures the consistency of image annotation by performing a Term Frequency-Inverse Document Frequency (TF-IDF) weight calculation for each n-gram. 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. So the main goal here is to put CNN-RNN together to create an automatic image captioning model that takes in an image as input and outputs a sequence of text that describes the image. This method is a Midge system based on maximum likelihood estimation, which directly learns the visual detector and language model from the image description dataset, as shown in Figure 1. The attention mechanism improves the model’s effect. So the main goal here is to put CNN-RNN together to create an automatic image captioning model that takes in an image as input and outputs a sequence of text that describes the image. [15] propose using a detector to detect objects in an image, classifying each candidate region and processing it by a prepositional relationship function and finally applying a conditional random field (CRF) prediction image tag to generate a natural language description. You et al. He, “Language models for image captioning: the quirks and what works,” 2015, K. Tran, X. 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