Attributes as Semantic Units between Natural Language and Visual Recognition
This work addresses the problem of multimodal interaction for researchers and practitioners in AI, though it appears incremental as it builds on existing attribute-based approaches.
The paper tackles the challenge of integrating computer vision and natural language processing by proposing attributes as a semantic bridge, enabling applications like recognizing novel visual categories, generating image descriptions, grounding language in visuals, and answering questions about images.
Impressive progress has been made in the fields of computer vision and natural language processing. However, it remains a challenge to find the best point of interaction for these very different modalities. In this chapter we discuss how attributes allow us to exchange information between the two modalities and in this way lead to an interaction on a semantic level. Specifically we discuss how attributes allow using knowledge mined from language resources for recognizing novel visual categories, how we can generate sentence description about images and video, how we can ground natural language in visual content, and finally, how we can answer natural language questions about images.