23 citations · 75 across the 34 of their papers we have counts for
35 papers
Bridging Human Evaluation to Infrared and Visible Image Fusion
Jinyuan Liu, Xingyuan Li, Qingyun Mei +5
Infrared and visible image fusion (IVIF) integrates complementary modalities to enhance scene perception. Current methods predominantly focus on optimizing handcrafted losses and o…
Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Jinyuan Liu, Zihang Chen, Zhu Liu +4
We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradat…
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond
Guanyao Wu, Haoyu Liu, Hongming Fu +4
Multi-modality image fusion, particularly infrared and visible, plays a crucial role in integrating diverse modalities to enhance scene understanding. Although early research prior…
DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion
Jinyuan Liu, Bowei Zhang, Qingyun Mei +6
Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each mo…
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement
Nan An, Long Ma, Guangchao Han +2
Deep learning-based low-light image enhancers have made significant progress in recent years, with a trend towards achieving satisfactory visual quality while gradually reducing th…
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
Zengxi Zhang, Zhiying Jiang, Long Ma +3
Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement met…
Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation
Yaohua Liu, Jiaxin Gao, Xuan Liu +3
Transfer attacks generate significant interest for real-world black-box applications by crafting transferable adversarial examples through surrogate models. Whereas, existing works…
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks
Zhiying Jiang, Xingyuan Li, Jinyuan Liu +2
Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but al…
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
Xingyuan Li, Yang Zou, Jinyuan Liu +4
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inher…
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Yaohua Liu, Jiaxin Gao, Xianghao Jiao +3
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications. However, prevailing AT methods, relying o…
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Jiaxin Gao, Ziyu Yue, Yaohua Liu +3
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination…
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…
Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion
Guanyao Wu, Hongming Fu, Jinyuan Liu +3
Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. Despite its advancements…
AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training
Xingyuan Li, Jinyuan Liu, Long Ma +2
Monocular 3D object detection plays a pivotal role in the field of autonomous driving and numerous deep learning-based methods have made significant breakthroughs in this area. Des…
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…
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Gehui Li, Jinyuan Liu, Long Ma +3
Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural…
Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration
Di Wang, Jinyuan Liu, Long Ma +2
Misalignments between multi-modality images pose challenges in image fusion, manifesting as structural distortions and edge ghosts. Existing efforts commonly resort to registering…
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…
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Zengxi Zhang, Zhiying Jiang, Jinyuan Liu +2
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods ma…
Multi-Spectral Image Stitching via Spatial Graph Reasoning
Zhiying Jiang, Zengxi Zhang, Jinyuan Liu +2
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary chall…
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
Risheng Liu, Jiaxin Gao, Xuan Liu +1
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-wo…
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios
Chenping Fu, Wanqi Yuan, Jiewen Xiao +2
Underwater degraded images greatly challenge existing algorithms to detect objects of interest. Recently, researchers attempt to adopt attention mechanisms or composite connections…