54 citations · 67 across the 13 of their papers we have counts for
5 papers · 1 filter
Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation
Xiaohan Cui, Long Ma, Tengyu Ma +3
Object detection in low-light scenarios has attracted much attention in the past few years. A mainstream and representative scheme introduces enhancers as the pre-processing for re…
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Jinyuan Liu, Zhu Liu, Guanyao Wu +5
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation. Early efforts focus on boosting the performance for only one task, \emph…
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
Long Ma, Dian Jin, Nan An +3
Enhancing images in low-light scenes is a challenging but widely concerned task in the computer vision. The mainstream learning-based methods mainly acquire the enhanced model by l…
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Zhu Liu, Jinyuan Liu, Guanyao Wu +3
Recently, multi-modality scene perception tasks, e.g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems. However, early effo…
Toward Fast, Flexible, and Robust Low-Light Image Enhancement
Long Ma, Tengyu Ma, Risheng Liu +2
Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown com…