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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.11207 |
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| _version_ | 1866917402960199680 |
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| author | Li, Xin Xu, Daoli Luo, Wei Xiang, Guoqiang Li, Haoran Zhuang, Chengyu Chen, Zhibo Guan, Jian Li, Weping Zhang, Weixia Sun, Wei Wang, Zhihua Zhu, Dandan Zhu, Chengguang Gupta, Ayush Agarwal, Rachit Das, Shouvik Das, Biplab Ch Ghosh, Amartya Fan, Kanglong Wen, Wen Zhai, Shuyan Zhi, Tianwu Zhang, Aoxiang Liu, Jianzhao Zhang, Yabin Wang, Jiajun Sun, Yipeng Lian, Kaiwei Yin, Banghao |
| author_facet | Li, Xin Xu, Daoli Luo, Wei Xiang, Guoqiang Li, Haoran Zhuang, Chengyu Chen, Zhibo Guan, Jian Li, Weping Zhang, Weixia Sun, Wei Wang, Zhihua Zhu, Dandan Zhu, Chengguang Gupta, Ayush Agarwal, Rachit Das, Shouvik Das, Biplab Ch Ghosh, Amartya Fan, Kanglong Wen, Wen Zhai, Shuyan Zhi, Tianwu Zhang, Aoxiang Liu, Jianzhao Zhang, Yabin Wang, Jiajun Sun, Yipeng Lian, Kaiwei Yin, Banghao |
| contents | This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how to evaluate the loss of semantic information from the human perspective, intending to promote the development of some new directions, like semantic coding, processing, and semantic-oriented optimization, etc. Unlike existing datasets of quality assessment, we form a dataset of human-oriented semantic quality assessment, termed the SeIQA dataset. This dataset is divided into three parts for this competition: (i) training data: 510 pairs of degraded images and their corresponding ground truth references; (ii) validation data: 80 pairs of degraded images and their corresponding ground-truth references; (iii) testing data: 160 pairs of degraded images and their corresponding ground-truth references. The primary objective of this challenge is to establish a new and powerful benchmark for human-oriented semantic image quality assessment. There are a total of 58 teams registered in this competition, and 6 teams submitted valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the SeIQA dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11207 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results Li, Xin Xu, Daoli Luo, Wei Xiang, Guoqiang Li, Haoran Zhuang, Chengyu Chen, Zhibo Guan, Jian Li, Weping Zhang, Weixia Sun, Wei Wang, Zhihua Zhu, Dandan Zhu, Chengguang Gupta, Ayush Agarwal, Rachit Das, Shouvik Das, Biplab Ch Ghosh, Amartya Fan, Kanglong Wen, Wen Zhai, Shuyan Zhi, Tianwu Zhang, Aoxiang Liu, Jianzhao Zhang, Yabin Wang, Jiajun Sun, Yipeng Lian, Kaiwei Yin, Banghao Computer Vision and Pattern Recognition This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how to evaluate the loss of semantic information from the human perspective, intending to promote the development of some new directions, like semantic coding, processing, and semantic-oriented optimization, etc. Unlike existing datasets of quality assessment, we form a dataset of human-oriented semantic quality assessment, termed the SeIQA dataset. This dataset is divided into three parts for this competition: (i) training data: 510 pairs of degraded images and their corresponding ground truth references; (ii) validation data: 80 pairs of degraded images and their corresponding ground-truth references; (iii) testing data: 160 pairs of degraded images and their corresponding ground-truth references. The primary objective of this challenge is to establish a new and powerful benchmark for human-oriented semantic image quality assessment. There are a total of 58 teams registered in this competition, and 6 teams submitted valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the SeIQA dataset. |
| title | LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11207 |