Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929231439593472 |
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| author | Zhang, Xiaofeng Xu, Zishan Tang, Hao Gu, Chaochen Chen, Wei Zhu, Shanying Guan, Xinping |
| author_facet | Zhang, Xiaofeng Xu, Zishan Tang, Hao Gu, Chaochen Chen, Wei Zhu, Shanying Guan, Xinping |
| contents | Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results. To address this, our study introduces "Enlighten-Your-Voice", a multimodal enhancement framework that innovatively enriches user interaction through voice and textual commands. This approach does not merely signify a technical leap but also represents a paradigm shift in user engagement. Our model is equipped with a Dual Collaborative Attention Module (DCAM) that meticulously caters to distinct content and color discrepancies, thereby facilitating nuanced enhancements. Complementarily, we introduce a Semantic Feature Fusion (SFM) plug-and-play module that synergizes semantic context with low-light enhancement operations, sharpening the algorithm's efficacy. Crucially, "Enlighten-Your-Voice" showcases remarkable generalization in unsupervised zero-shot scenarios. The source code can be accessed from https://github.com/zhangbaijin/Enlighten-Your-Voice |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10109 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement Zhang, Xiaofeng Xu, Zishan Tang, Hao Gu, Chaochen Chen, Wei Zhu, Shanying Guan, Xinping Computer Vision and Pattern Recognition Artificial Intelligence Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results. To address this, our study introduces "Enlighten-Your-Voice", a multimodal enhancement framework that innovatively enriches user interaction through voice and textual commands. This approach does not merely signify a technical leap but also represents a paradigm shift in user engagement. Our model is equipped with a Dual Collaborative Attention Module (DCAM) that meticulously caters to distinct content and color discrepancies, thereby facilitating nuanced enhancements. Complementarily, we introduce a Semantic Feature Fusion (SFM) plug-and-play module that synergizes semantic context with low-light enhancement operations, sharpening the algorithm's efficacy. Crucially, "Enlighten-Your-Voice" showcases remarkable generalization in unsupervised zero-shot scenarios. The source code can be accessed from https://github.com/zhangbaijin/Enlighten-Your-Voice |
| title | Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2312.10109 |