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Multiple modality data bring new challenges for sentiment analysis, as combining varieties of information in an effective manner is a rigorous task. Previous works do not effectively utilize the relationship and influence between texts and images. This paper proposes a fusion-extraction network model for multimodal sentiment analysis. First, our model uses an interactive information fusion mechanism to interactively learn the visual-specific textual representations and the textual-specific visual representations. Then, we propose an information extraction mechanism to extract valid information and filter redundant parts for the specific textual and visual representations. The experimental results on two public multimodal sentiment datasets show that our model outperforms existing state-of-the-art methods.

Human-object interaction (HOI) detection is an important task for understanding human activity. Graph structure is appropriate to denote the HOIs in the scene. Since there is an subordination between human and object-human play subjective role and object play objective role in HOI, the relations between homogeneous entities and heterogeneous entities in the scene should also not be equally the same. However, previous graph models regard human and object as the same kind of nodes and do not consider that the messages are not equally the same between different entities. In this work, we address such a problem for HOI task by proposing a heterogeneous graph network that models humans and objects as different kinds of nodes and incorporates intra-class messages between homogeneous nodes and inter-class messages between heterogeneous nodes. In addition, a graph attention mechanism based on the intra-class context and inter-class context is exploited to improve the learning. Extensive experiments on the benchmark datasets V-COCO and HICO-DET verify the effectiveness of our method and demonstrate the importance to extract intra-class and inter-class messages which are not equally the same in HOI detection.