It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective target areas. We believe the LLVIP dataset will contribute to the community of computer vision by promoting image fusion, pedestrian detection and image-to-image translation in very low-light applications.
This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.
Read the full paper
Access the original peer-reviewed research via OpenAlex.
| Category | 🤖 Artificial Intelligence |
| Published | Oct 01, 2021 |
| Journal | Research Journal |
| Authors | Xinyu Jia, Chuang Zhu, Minzhen Li, Wenqi Tang, Wenli Zhou |
| DOI | 10.1109/iccvw54120.2021.00389 |
| Citations | 709 |
| Source | OpenAlex |