In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 million sentences describing over 40 million images crawled and downloaded from publicly available Pins (i.e. an image with sentence descriptions uploaded by users) on Pinterest. This dataset is more than 200 times larger than MS COCO, the standard large-scale image dataset with sentence descriptions. In addition, we construct an evaluation dataset to directly assess the effectiveness of word embeddings in terms of finding semantically similar or related words and phrases. The word/phrase pairs in this evaluation dataset are collected from the click data with millions of users in an image search system, thus contain rich semantic relationships. Based on these datasets, we propose and compare several Recurrent Neural Networks (RNNs) based multimodal (text and image) models. Experiments show that our model benefits from incorporating the visual information into the word embeddings, and a weight sharing strategy is crucial for learning such multimodal embeddings. The project page is: http://www.stat.ucla.edu/~junhua.mao/multimodal_embedding.html

本研究旨在使用文本和视觉信息进行有效的单词嵌入训练和评估。研究人员提出了一个大规模数据集，其中包含300万语句，描述了来自Pinterest的超过4000万张图像。该研究还报道了一种基于RNN的多模态模型，通过在嵌入中整合视觉信息，该模型可以找到语义相似或相关的单词和短语。经验表明，共享策略对于学习这种多模态嵌入至关重要。

利用大规模网络标注图像进行多模态词嵌入的训练和评估