# Vision-to-Language Tasks Based on Attributes and Attention Mechanism

## TL;DR

This paper introduces a novel attention-based approach for vision-to-language tasks that leverages text-guided and semantic-guided attention mechanisms to improve image captioning and visual question answering by better capturing semantic concepts and relationships.

## Contribution

It proposes a dual-level attention network that effectively integrates high-level semantic concepts and spatial relationships to enhance vision-to-language task performance.

## Key findings

- Achieved superior results in image captioning tasks.
- Improved accuracy in visual question answering.
- Demonstrated effectiveness of combined attention mechanisms.

## Abstract

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.