27 citations · 62 across the 12 of their papers we have counts for
9 papers · 1 filter
Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework
Zhu Liu, Zihang Chen, Jinyuan Liu +3
The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainst…
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
Zhu Liu, Jinyuan Liu, Benzhuang Zhang +3
Infrared and visible image fusion is a powerful technique that combines complementary information from different modalities for downstream semantic perception tasks. Existing learn…
Bilevel Generative Learning for Low-Light Vision
Yingchi Liu, Zhu Liu, Long Ma +4
Recently, there has been a growing interest in constructing deep learning schemes for Low-Light Vision (LLV). Existing techniques primarily focus on designing task-specific and dat…
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…
A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion
Risheng Liu, Zhu Liu, Jinyuan Liu +2
Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. Howev…
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…
Searching a Compact Architecture for Robust Multi-Exposure Image Fusion
Zhu Liu, Jinyuan Liu, Guanyao Wu +3
In recent years, learning-based methods have achieved significant advancements in multi-exposure image fusion. However, two major stumbling blocks hinder the development, including…
Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion
Yuhui Wu, Zhu Liu, Jinyuan Liu +2
Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However…
Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision
Risheng Liu, Zhu Liu, Pan Mu +2
In recent years, there has been a growing interest in combining learnable modules with numerical optimization to solve low-level vision tasks. However, most existing approaches foc…