234 citations · 341 across the 30 of their papers we have counts for
31 papers
RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels
Chengzhou Li, Ping Guo, Guanchen Meng +7
Object detection in sonar images is a key technology in underwater detection systems. Compared to natural images, sonar images contain fewer texture details and are more susceptibl…
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…
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
Jinyuan Liu, Guanyao Wu, Zhu Liu +6
Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representat…
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
Pan Mu, Zhiying Du, Jinyuan Liu +1
In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion. Nonetheless, prevailing approaches often involve directly feeding…
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…
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…
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
Xiaoke Shang, Gehui Li, Zhiying Jiang +3
The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historical…
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
Zengxi Zhang, Zeru Shi, Zhiying Jiang +1
Due to the uneven absorption of different light wavelengths in aquatic environments, underwater images suffer from low visibility and clear color deviations. With the advancement o…
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…
Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion
Jiawei Li, Jiansheng Chen, Jinyuan Liu +1
Infrared and visible image fusion has gradually proved to be a vital fork in the field of multi-modality imaging technologies. In recent developments, researchers not only focus on…
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…
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…
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…
An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection
Di Wang, Jinyuan Liu, Risheng Liu +1
This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IV…