Target-dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots
This work addresses the specific problem of multimodal language comprehension for domestic service robots, representing an incremental improvement over existing UNITER-based methods.
The paper tackles the problem of domestic service robots' insufficient natural language interaction by addressing ambiguities in human instructions, particularly insufficient modeling of object relationships in referring expressions. The proposed Target-dependent UNITER model outperforms baseline methods on two standard datasets with improved classification accuracy.
Currently, domestic service robots have an insufficient ability to interact naturally through language. This is because understanding human instructions is complicated by various ambiguities and missing information. In existing methods, the referring expressions that specify the relationships between objects are insufficiently modeled. In this paper, we propose Target-dependent UNITER, which learns the relationship between the target object and other objects directly by focusing on the relevant regions within an image, rather than the whole image. Our method is an extension of the UNITER-based Transformer that can be pretrained on general-purpose datasets. We extend the UNITER approach by introducing a new architecture for handling the target candidates. Our model is validated on two standard datasets, and the results show that Target-dependent UNITER outperforms the baseline method in terms of classification accuracy.