Low-Quality Image Detection by Hierarchical VAE
<|reference_start|>Low-Quality Image Detection by Hierarchical VAE: To make an employee roster, photo album, or training dataset of generative models, one needs to collect high-quality images while dismissing low-quality ones. This study addresses a new task of unsupervised detection of low-quality images. We propose a method that not only detects low-quality images with various types of degradation but also provides visual clues of them based on an observation that partial reconstruction by hierarchical variational autoencoders fails for low-quality images. The experiments show that our method outperforms several unsupervised out-of-distribution detection methods and also gives visual clues for low-quality images that help humans recognize them even in thumbnail view.<|reference_end|>
@article{nanaumi2024low-quality,
    title={Low-Quality Image Detection by Hierarchical VAE},
    author={Tomoyasu Nanaumi, Kazuhiko Kawamoto, Hiroshi Kera},
    journal={arXiv preprint arXiv:2408.10885},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10885},
    primaryClass={cs.CV}
}