Recently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The existing models counting on this framework merely employ one kind of CNNs, e.g., ResNet or Inception-X, which describe image contents from only one specific view point. Thus, the semantic meaning of an input image cannot be comprehensively understood, which restricts the performance of captioning. In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning. The fusion process in our model can exploit the interactions among the outputs of the image encoders and then generate new compact yet informative representations for the decoder. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets a new state-of-the-art for image captioning.

Recurrent Fusion Network for Image Captioning

Dual Global Enhanced Transformer for image captioning

Existing image captioning approaches fail to generate fine-grained captions due to the lack of rich encoding representation of an image. In this paper, we present an attentive contextual network (ACN) to learn the spatially transformed image features and dense multi-scale contextual information of an image to generate semantically meaningful captions. At first, we construct deformable network on intermediate layers of convolutional neural network (CNN) to cultivate spatial invariant features. And the multi-scale contextual features are produced by employing contextual network on top of last layers of CNN. Then, we exploit attention mechanism on contextual network to extract dense contextual features. Further, the extracted spatial and contextual features are combined to encode the holistic representation of an image. Finally, a multi-stage caption decoder with visual attention module is incorporated to generate fine-grained captions. The performance of the proposed approach is demonstrated on COCO dataset, the largest dataset for image captioning.

Attentive Contextual Network for Image Captioning

A good document summary should summarize the core content of the text. Research on automatic text summarization attempts to solve this problem. The encoder-decoder model is widely used in text summarization research. Soft attention is used to obtain the required contextual semantic information during decoding. However, due to the lack of access to the key features, the generated summary deviates from the core content. In this paper, we proposed an encoder-decoder model based on a double attention pointer network (DAPT). In DAPT, the self-attention mechanism collects key information from the encoder, the soft attention and the pointer network generate more coherent core content, and the fusion of both generates accurate and coherent summaries. In addition, the improved coverage mechanism is used to address the repetition problem and improve the quality of the generated summaries. Simultaneously, scheduled sampling and reinforcement learning (RL) are combined to generate new training methods to optimize the model. Experiments on the CNN/Daily Mail dataset and the LCSTS dataset show that our model performs as well as many state-of-the-art models. The experimental analysis shows that our model achieves higher summarization performance and reduces the occurrence of repetition.

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Text Summarization Method Based on Double Attention Pointer Network

Generating textual descriptions of images has been an important topic in computer vision and natural language processing. A number of techniques based on deep learning have been proposed on this topic. These techniques use human-annotated images for training and testing the models. These models require a large number of training data to perform at their full potential. Collecting human generated images with associative captions is expensive and time-consuming. In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model. We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images. We use an attention-based image captioning method trained on both real and synthetic images to generate the captions. We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics. We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training.

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Text to Image Synthesis for Improved Image Captioning

Most existing image captioning methods use only the visual information of the image to guide the generation of captions, lack the guidance of effective scene semantic information, and the current visual attention mechanism cannot adjust the focus intensity on the image. In this article, we first propose an improved visual attention model. At each timestep, we calculated the focus intensity coefficient of the attention mechanism through the context information of the model, then automatically adjusted the focus intensity of the attention mechanism through the coefficient to extract more accurate visual information. In addition, we represented the scene semantic knowledge of the image through topic words related to the image scene, then added them to the language model. We used the attention mechanism to determine the visual information and scene semantic information that the model pays attention to at each timestep and combined them to enable the model to generate more accurate and scene-specific captions. Finally, we evaluated our model on Microsoft COCO (MSCOCO) and Flickr30k standard datasets. The experimental results show that our approach generates more accurate captions and outperforms many recent advanced models in various evaluation metrics.

Integrating Scene Semantic Knowledge into Image Captioning

The synergy of double attention: Combine sentence-level and word-level attention for image captioning

Automatically generating a human-like description for a given image is a potential research in artificial intelligence, which has attracted a great of attention recently. Most of the existing attention methods explore the mapping relationships between words in sentence and regions in image, such unpredictable matching manner sometimes causes inharmonious alignments that may reduce the quality of generated captions. In this paper, we make our efforts to reason about more accurate and meaningful captions. We first propose word attention to improve the correctness of visual attention when generating sequential descriptions word-by-word. The special word attention emphasizes on word importance when focusing on different regions of the input image, and makes full use of the internal annotation knowledge to assist the calculation of visual attention. Then, in order to reveal those incomprehensible intentions that cannot be expressed straightforwardly by machines, we introduce a new strategy to inject external knowledge extracted from knowledge graph into the encoder-decoder framework to facilitate meaningful captioning. Finally, we validate our model on two freely available captioning benchmarks: Microsoft COCO dataset and Flickr30k dataset. The results demonstrate that our approach achieves state-of-the-art performance and outperforms many of the existing approaches.

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Boost image captioning with knowledge reasoning

Boost Image Captioning with Knowledge Reasoning

Existing attention models of image captioning typically extract only word-level attention information. i.e., the attention mechanism extracts local attention information from the image to generate the current word. We propose an image captioning approach based on self-attention to utilize image features more effectively. The self-attention mechanism can extract sentence-level attention information with richer visual representation from images. Furthermore, we propose a double attention model. The model combines sentence-level and word-level attention information to better simulate human perception system. We implement supervision and optimization in the intermediate stage of the model to solve over-fitting and information interference problems, and we apply reinforcement learning to two-stage training to optimize the evaluation metrics of the model. Finally, we evaluate our model on MSCOCO dataset. The experimental results show that our approach can generate more accurate and richer captions, and outperforms many state-of-the-art image captioning approaches on various evaluation metrics.

Haiyang Wei

Papers

Integrating Scene Semantic Knowledge into Image Captioning

The synergy of double attention: Combine sentence-level and word-level attention for image captioning

Boost image captioning with knowledge reasoning

Boost Image Captioning with Knowledge Reasoning

Image Captioning Based On Sentence-Level And Word-Level Attention