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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…
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…
Learning Heavily-Degraded Prior for Underwater Object Detection
Chenping Fu, Xin Fan, Jiewen Xiao +3
Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as ha…
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…
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…
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining
Zhiying Jiang, Risheng Liu, Shuzhou Yang +2
Rain streaks significantly decrease the visibility of captured images and are also a stumbling block that restricts the performance of subsequent computer vision applications. The…
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…
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation
Xianghao Jiao, Yaohua Liu, Jiaxin Gao +3
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of…
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes
Xingyuan Li, Jinyuan Liu, Yixin Lei +3
3D object detection plays a crucial role in numerous intelligent vision systems. Detection in the open world inevitably encounters various adverse scenes, such as dense fog, heavy…
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…
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…
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Xiaofeng Liu, Jiaxin Gao, Yaohua Liu +2
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understand…