Deep neural networks have achieved promising results in automatic image captioning due to their effective representation learning and context-based content generation capabilities. As a prominent type of deep features used in many of the recent image captioning methods, the well-known bottomup features provide a detailed representation of different objects of the image in comparison with the feature maps directly extracted from the raw image. However, the lack of high-level semantic information about the relationships between these objects is an important drawback of bottom-up features, despite their expensive and resource-demanding extraction procedure. To take advantage of visual relationships in caption generation, this paper proposes a deep neural network architecture for image captioning based on fusing the visual relationships information extracted from an image's scene graph with the spatial feature maps of the image. A multi-modal reward function is then introduced for deep reinforcement learning of the proposed network using a combination of language and vision similarities in a common embedding space. The results of extensive experimentation on the MSCOCO dataset show the effectiveness of using visual relationships in the proposed captioning method. Moreover, the results clearly indicate that the proposed multi-modal reward in deep reinforcement learning leads to better model optimization, outperforming several state-of-the-art image captioning algorithms, while using light and easy to extract image features. A detailed experimental study of the components constituting the proposed method is also presented.

翻译：深度神经网络因其有效的表示学习和基于上下文的生成能力，在自动图像描述任务中取得了显著成果。作为近期诸多图像描述方法中广泛使用的重要深度特征类型，著名的自底向上特征相较于直接从原始图像提取的特征图，能够提供图像中不同物体的详细表示。然而，尽管这类特征的提取过程昂贵且资源消耗大，其缺乏关于物体之间关系的高层语义信息却是一个重要缺陷。为利用视觉关系辅助描述生成，本文提出一种基于融合图像场景图中视觉关系信息与空间特征图的深度神经网络架构。进而引入多模态奖励函数，通过语言与视觉相似性在共同嵌入空间中的组合，对所提网络进行深度强化学习。在MSCOCO数据集上的大量实验结果表明，在所提描述方法中利用视觉关系具有有效性。此外，结果清晰表明，深度强化学习中提出的多模态奖励有助于实现更优的模型优化，在采用轻量且易提取图像特征的同时，性能超越若干当前最先进的图像描述算法。本文还对各组成部件进行了详细的实验研究。