Vision-to-Language Tasks Based on Attributes and Attention Mechanism
This work addresses the problem of semantic gaps in vision-to-language integration for researchers and practitioners, though it appears incremental as it builds on existing attention-based methods.
The paper tackled vision-to-language tasks by introducing a dual attention mechanism that uses text-guided and semantic-guided attention to better align image regions with language elements, resulting in excellent performance in image captioning and visual question answering experiments.
Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts and subtle relationships between image regions and natural language elements. To make full use of these information, this paper attempt to exploit the text guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language. Our method includes two level attention networks. One is the text-guided attention network which is used to select the text-related regions. The other is SA network which is used to highlight the concept-related regions and the region-related concepts. At last, all these information are incorporated to generate captions or answers. Practically, image captioning and visual question answering experiments have been carried out, and the experimental results have shown the excellent performance of the proposed approach.