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CE ^{3} USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection
Qingyao Wu;Jiaxin Xie;Zhenqi Fu;Xiaotong Tu;Yue Huang;Xinghao Ding
IEEE Journal of Oceanic Engineering
Year: 2025 | Early Access Article |
Underwater salient object detection (USOD) aims to identify the most crucial elements in underwater environments, holding significant potential for underwater exploration. Existing methods often overlook light degradation or involve larger network sizes, which are unsuitable for underwater mobile platforms and pose challenges to implement in practice. Given the importance of low-complexity algorit...Show More
Lightweight SAR Ship Detection via Pearson Correlation and Nonlocal Distillation
Yinuo Zhang;Weimin Cai;Jingchao Guo;Hangyang Kong;Yue Huang;Xinghao Ding
Aiming to the challenge of efficient synthetic aperture radar (SAR) ship detection, knowledge distillation recently gained increasing attention as an effective model lightweight approach. SAR ship detection faces challenges including small target detection and complex background clutter. Most existing knowledge distillation methods impose overly strict constraints on the student model, leading to ...Show More
Toward Generalizable Pansharpening: Conditional Flow-Based Learning Guided by Implicit High-Frequency Priors
Yingying Wang;Hui Zheng;Feifei Li;Yunlong Lin;Linyu Fan;Xuanhua He;Yue Huang;Xinghao Ding
The goal of pansharpening is to restore the missing high-frequency details in the low-resolution multispectral (LRMS) image to generate its high-resolution multispectral (HRMS) counterpart by exploiting the high-resolution panchromatic (PAN) image as guidance. Previous research has predominantly focused on improving pansharpening performance for single satellites, often neglecting the challenge of...Show More
Learning Diffusion High-Quality Priors for Pan-Sharpening: A Two-Stage Approach With Time-Aware Adapter Fine-Tuning
Yingying Wang;Yunlong Lin;Xuanhua He;Hui Zheng;Keyu Yan;Linyu Fan;Yue Huang;Xinghao Ding
Pan-sharpening aims to enhance the spatial resolution of the low-resolution multispectral (LRMS) image by incorporating high-frequency details from the panchromatic (PAN) image, while maintaining the spectral qualities of the LRMS image. Recent advancements in diffusion models have shown remarkable capabilities in image restoration and generation. However, simply applying diffusion models in pan-s...Show More
Random Response-Based SAR Purification Defense
Zixu Lin;Jiewei Zheng;Jingchao Guo;Huangxing Lin;Yue Huang;Xinghao Ding
Deep learning-driven synthetic aperture radar automatic target recognition (SAR ATR) has gained increasing attention recently. However, current methods remain highly vulnerable to adversarial attacks, limiting their practical application. Most adversarial defense methods rely on adversarial training or attack detection, which tend to overfit and result in poor robustness against different attack t...Show More
Domain Adaptive Oriented Object Detection From Optical to SAR Images
Hailiang Huang;Jingchao Guo;Huangxing Lin;Yue Huang;Xinghao Ding
Oriented object detection in synthetic aperture radar (SAR) images presents significant challenges due to the scarcity of labeled data. In contrast, acquiring labeled optical remote sensing images is considerably easier. This article proposes a domain adaptive oriented object detection (DAOOD) model, termed the pixel-instance information transfer-based model (PITM). PITM aims to transfer knowledge...Show More
Generalizable Prompts Guided by Image-Redundant Separation for Vehicle Reidentification
Zhenyu Kuang;Lidong Cheng;Hongyang Zhang;Yue Huang;Xinghao Ding
Vehicle reidentification (reID) is a critical computer vision task with applications in video surveillance and autonomous vehicles. While significant progress has been made in recent years, domain generalization (DG) in reID remains a challenging and valuable research direction. Learning discriminative features that capture the intrinsic characteristics of vehicles, rather than domain-specific det...Show More
Filling and Disentanglement: Towards Low- and High-Order Parallel Single-Domain Generalization for SAR Ship Detection
Yuxuan Yuan;Luyao Tang;Ying Xu;Chuyang Lin;Chaoqi Chen;Yue Huang;Xinghao Ding
IEEE Transactions on Aerospace and Electronic Systems
Year: 2024 | Early Access Article |
Unsupervised Domain Adaptation (UDA) has demonstrated promising results in the ship detection tasks for Synthetic Aperture Radar (SAR) images under distribution shift. However, its effectiveness depends on the availability of unlabeled target data to mitigate domain discrepancies during the training process, which poses a challenge in real-world scenarios, as acquiring SAR data from the target dom...Show More
Efficient Training Acceleration via Sample-Wise Dynamic Probabilistic Pruning
Data pruning is observed to substantially reduce the computation and memory costs of model training. Previous studies have primarily focused on constructing a series of coresets with representative samples by leveraging predefined rules for evaluating sample importance. Learning dynamics and selection bias, however, are rarely being considered. In this letter, a novel Sample-wise Dynamic Probabili...Show More
Class Incremental Aerial Scene Recognition Under Long-Tailed Distribution
Gang Yang;Haizhou Ding;Xiaotong Tu;Lexing Huang;Yue Huang;Xinghao Ding
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Deep learning excels in aerial scene recognition (ASR) but struggles with learning from sequential data due to catastrophic forgetting. Class incremental learning (CIL) can address this but often assumes a balanced data distribution. Real-world aerial scenes exhibit long-tailed properties, the undesirable bias toward the head classes as well as overfitting for the tail classes aggravate the challe...Show More
S3AHI: Source-Free Domain Adaptive Small Object Detection with Slicing Aided Hyper Inference
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
The time-consuming and laborious annotation of small objects has resulted in a relative scarcity of datasets specifically designed for small objects. Additionally, variations in data acquisition devices and application scenarios often cause a domain shift between source-trained data and target data. Unsupervised Domain Adaptation (UDA) is extensively applied to alleviate the domain shift between t...Show More
An Adaptive Spatio-Temporal Graph Structure Learning Model for Lithium-Ion Battery Pack State of Health Estimation
Canxing Lai;Xiaotong Tu;Andreas Jakobsson;Xinghao Ding;Yue Huang
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Current research on lithium-ion battery state of health (SOH) estimation predominantly focuses on a single battery, not on an entire battery pack, which makes these methods inadequate for describing the SOH of energy systems that work in real-life situations. Furthermore, the current SOH estimation methods using graph neural networks (GNNs) inherently adopt suboptimal graph construction approaches...Show More
Leveraging Foreground Collaboration and Augmentation for Industrial Anomaly Detection
Xiaolu Chen;Haote Xu;Jiaxiang Wang;Xiaotong Tu;Xinghao Ding;Yue Huang
Reconstruction-based methods, as one of the mainstream and advanced methods for anomaly detection, have attracted significant attention in the academic community. Although these methods may achieve good performance on some ideal industrial datasets, background factors have considerable influence on detecting anomalies due to a complex and ever-changing environment, resulting in overkill and false ...Show More
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Haote Xu;Yunlong Zhang;Xiaolu Chen;Changxing Jing;Liyan Sun;Yue Huang;Xinghao Ding
The primary challenge faced by reconstruction-based anomaly detection (AD) methods is that neural networks exhibit strong generalization, resulting in a high probability and accuracy of anomaly reconstruction. Several existing methods attempt to alleviate this problem by randomly masking partial image regions and reconstructing the image from partial inpaintings. However, local masking in spatial ...Show More
Cross-Modality Interaction Network for Pan-Sharpening
Yingying Wang;Xuanhua He;Yuhang Dong;Yunlong Lin;Yue Huang;Xinghao Ding
Pan-sharpening seeks to generate a high-resolution multispectral (HRMS) image by merging the high-resolution panchromatic (PAN) image and its low-resolution multispectral (LRMS) counterpart. The main challenge lies in enhancing modality-aware features and efficiently integrating complementary information between PAN and MS pairs. To achieve desired fusion results, it is crucial to fully utilize bo...Show More
Diffusion-Based Continuous Feature Representation for Infrared Small-Dim Target Detection
Linyu Fan;Yingying Wang;Guoliang Hu;Feifei Li;Yuhang Dong;Hui Zheng;Changqing Lin;Yue Huang;Xinghao Ding
Infrared small-dim target detection (ISDTD) plays a pivotal role in missions involving rescue, surveillance, and early warning systems. Despite remarkable strides made by existing methods, certain limitations still hinder the detection accuracy, including deficiency in high-resolution (HR) representation, inadequacy in addressing dim targets, and difficulty in tackling low-contrast targets against...Show More
MASNet: A Robust Deep Marine Animal Segmentation Network
Zhenqi Fu;Ruizhe Chen;Yue Huang;En Cheng;Xinghao Ding;Kai-Kuang Ma
Marine animal studies are of great importance to human beings and instrumental to many research areas. How to identify such animals through image processing is a challenging task that leads to marine animal segmentation (MAS). Although deep neural networks have been widely applied for object segmentation, few of them consider the complex imaging condition in the water and the camouflage property o...Show More
Effective HRRP data augmentation by contrastive adversarial training
IET International Radar Conference (IRC 2023)
Year: 2023 | Conference Paper |
Recently, deep learning methods have significantly improved the recognition performance of high-resolution range profiles (HRRP). However, these methods often assume the availability of sufficient HRRP data, which is challenging in real-world applications, particularly for uncooperative targets. As a result, existing deep learning based HRRP recognition algorithms experience a significant drop in ...Show More
Efficient Sound Localization Algorithm Under Strong Interference
2023 6th International Conference on Information Communication and Signal Processing (ICICSP)
Year: 2023 | Conference Paper |
Sound source localization using a drone has demon-strated gratifying results, such as in fault detection, rescue mission, surveillance. At present, the existing classical sound localization algorithms often yield low positioning accuracy. Besides, the high time cost of calculation makes these algorithms difficult to be applied in practice. In this paper, we propose a algorithm that utilizes the in...Show More
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Chaoqi Chen;Luyao Tang;Leitian Tao;Hong-Yu Zhou;Yue Huang;Xiaoguang Han;Yizhou Yu
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel domains and object classes often occur. In this paper, we study a practical problem of Domain Generalization under Category Shift (DGCS), which aims to simultaneously detect unknown-class samples and classify known-class s...Show More
Joint Image and Feature Levels Disentanglement for Generalizable Vehicle Re-identification
Domain generalization (DG), which doesn’t require any data from target domains during training, is more challenging but practical than unsupervised domain adaptation (UDA). Since different vehicles of the same type have a similar appearance, neural networks always rely on a small amount of useful information to distinguish them, meaning that is more significant to remove ID-unrelated information f...Show More
Adaptive Ship Detection From Optical to SAR Images
Recent advances in synthetic aperture radar (SAR) ship detection have witnessed remarkable success by using large-scale annotated datasets. However, the annotation of SAR images requires strong domain-specific expertise, significantly hindering the prompt adoption of modern object detectors in this regime. Compared to SAR data, optical data in geoscience are considerably easier to label. Motivated...Show More
Underwater Image Enhancement and Super-Resolution Using Implicit Neural Networks
Xueye Chu;Zhenqi Fu;Shaocong Yu;Xiaotong Tu;Yue Huang;Xinghao Ding
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Underwater images are often notably degraded by light scattering and absorption. To improve image quality and object details, we present a novel unsupervised underwater image enhancement and super-resolution method using implicit neural networks. Concretely, taking low-resolution coordinates as the inputs, we first leverage Fourier feature mapping to encode the coordinates. Then, three implicit ne...Show More
Radar HRRP Unseen Class Recognition Based on the Joint Dictionary Learning
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Existing task settings and methods for radar high resolution range profile (HRRP) recognition are limited in addressing open challenges. To avoid labor-intensive data collection and model retraining, we formulate a new task called HRRP unseen class recognition, where the testing classes are unknown during training. To perform this task, we utilize metric learning to explore the potential informati...Show More
Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances
Zhenqi Fu;Yan Yang;Xiaotong Tu;Yue Huang;Xinghao Ding;Kai-Kuang Ma
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (82)
Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted pri...Show More
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