text: Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the inter-modal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semi-supervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multi-modal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.
  @cite_27: A novel approach COSLAQ for cross modal similarity learning with active queries is proposed.Disagreement-based active query strategy explores the most valuable supervised information.Uncertainty of similarity learning model is utilized to avoid querying outliers and noises. In real applications, data is usually collected from heterogeneous sources and represented with multiple modalities. To facilitate the analysis of such complex tasks, it is important to learn an effective similarity across different modalities. Existing similarity learning methods usually requires a large number of labeled training examples, leading to high labeling costs. In this paper, we propose a novel approach COSLAQ for active cross modal similarity learning, which actively queries the most important supervised information based on the disagreement among different intra-modal and inter-modal similarities. Furthermore, the closeness to decision boundary of similarity is utilized to avoid querying outliers and noises. Experiments on benchmark datasets demonstrate that the proposed method can reduce the labeling cost effectively.
summary: Although these methods have achieved great success in multi-modal learning, most of them need a mass of training data to learn the complex correlation between objects from different modality. To reduce the demand of training data, @cite_27 proposed an active similarity learning model for cross-modal data. Nevertheless, without extra information, the improvement is limited.