# News Cover Assessment via Multi-task Learning

## TL;DR

This paper introduces a multi-task learning approach using a modified DeepLabv3+ model to assess news cover images by evaluating clarity and object salience, aiming to improve online news presentation.

## Contribution

It presents a novel end-to-end multi-task network for simultaneous image clarity assessment and semantic segmentation tailored for news cover evaluation.

## Key findings

- The model outperforms single-task baselines on the CIA dataset.
- It effectively captures important image content for news cover assessment.
- The approach integrates image quality and content analysis in a unified framework.

## Abstract

Online personalized news product needs a suitable cover for the article. The news cover demands to be with high image quality, and draw readers' attention at same time, which is extraordinary challenging due to the subjectivity of the task. In this paper, we assess the news cover from image clarity and object salience perspective. We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment. The proposed network is based on a modified DeepLabv3+ model. The network backbone is used for multiple scale spatial features exaction, followed by two branches for image clarity assessment and semantic segmentation, respectively. The experiment results show that the proposed model is able to capture important content in images and performs better than single-task learning baselines on our proposed game content based CIA dataset.