Search Results for author:
Found 72 papers, 34 papers with code
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model
In this work, we extend the diffusion model's application to the HSI-CD field and propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD).
VTAE: Variational Transformer Autoencoder with Manifolds Learning
This weak projection, however, can be addressed by a Riemannian metric, and we show that geodesics computation and accurate interpolations between data samples on the Riemannian manifold can substantially improve the performance of deep generative models.
LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking
In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prior to search for the plausible deformation of landmark.
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images.
Deep Learning Methods for Calibrated Photometric Stereo and Beyond: A Survey
We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set.
Dynamic Event-Triggered Discrete-Time Linear Time-Varying System with Privacy-Preservation
Moreover, the privacy of the system is analyzed to ensure the security of the real data.
Stealthy Measurement-Aided Pole-Dynamics Attacks with Nominal Models
Thirdly, the performance of the proposed MAPDAs method is explored using convergence of multivariate measurements, and MAPDAs with model mismatch have the same stealthiness and similar destructiveness as TPDAs.
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples.
Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation
In this paper, a novel framework named PCSR is proposed to tackle SFDA via a novel intra-class Polycentric Clustering and Structural Regularization strategy.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network
In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed.
Binary Representation via Jointly Personalized Sparse Hashing
Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space.
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions.
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour
Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism.
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation
This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.
giMLPs: Gate with Inhibition Mechanism in MLPs
As for the gate with inhibition MLPs on DeBERTa (giDeBERTa) fine-tuning, we find it can achieve appealing results on most parts of NLU tasks without any extra pretraining again.
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects.
CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images
Several state-of-the-art segmentation methods are evaluated on this dataset.
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images
In this paper, we introduce an unsupervised cancer segmentation framework for histology images.
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus.
A lightweight multi-scale context network for salient object detection in optical remote sensing images
Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module.
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively.
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping
For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters.
Neuroplastic graph attention networks for nuclei segmentation in histopathology images
The architecture is comprised of a novel neuroplastic graph attention network based on residual graph attention layers and concurrent optimization of the graph structure representing multiple magnification levels of the histopathological image.
Colour alignment for relative colour constancy via non-standard references
This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.
Ranked #12 on
Object Detection In Aerial Images
on DOTA
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly
With this dataset, the proposed system has successfully achieved the detection accuracy of 73. 3%.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment.
Secure Control of Networked Control Systems Using Dynamic Watermarking
We here investigate secure control of networked control systems developing a new dynamic watermarking (DW) scheme.
Multi-scale Edge-based U-shape Network for Salient Object Detection
Deep-learning based salient object detection methods achieve great improvements.
Robust Ensembling Network for Unsupervised Domain Adaptation
In this paper, we propose a Robust Ensembling Network (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer.
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images
Second, most of the standard methods used hand-crafted features, and do not work well on the detection of objects parts of which are missing.
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB).
Incorporating Lambertian Priors into Surface Normals Measurement
In order to address this challenge, we here propose a photometric stereo network that incorporates Lambertian priors to better measure the surface normal.
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods.
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning.
Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics
Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision.
Learning the Precise Feature for Cluster Assignment
Based on this, we propose a general-purpose deep clustering framework which radically integrates representation learning and clustering into a single pipeline for the first time.
Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery
The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation
Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation.
Dual Discriminator Adversarial Distillation for Data-free Model Compression
Then the generated samples are used to train the compact student network under the supervision of the teacher.
Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images
In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings.
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals
Challenges of exoskeletons motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission.
Learning Calibrated-Guidance for Object Detection in Aerial Images
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification
Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed.
Towards advancing the earthquake forecasting by machine learning of satellite data
Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range.
Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images
Synthetic generation of three-dimensional cell models from histopathological images aims to enhance understanding of cell mutation, and progression of cancer, necessary for clinical assessment and optimal treatment.
Class balanced underwater object detection dataset generated by class-wise style augmentation
CWSA is a new kind of data augmentation technique which augments the training data for the minority classes by generating various colors, textures and contrasts for the minority classes.
Associated Spatio-Temporal Capsule Network for Gait Recognition
It is a challenging task to identify a person based on her/his gait patterns.
Multimodal Gait Recognition for Neurodegenerative Diseases
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization
Recent technological progress in the development of Unmanned Aerial Vehicles (UAVs) together with decreasing acquisition costs make the application of drone fleets attractive for a wide variety of tasks.
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training.
Distributed Fusion Estimation for Stochastic Uncertain Systems with Network-Induced Complexity and Multiple Noise
This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties.
Structured Context Enhancement Network for Mouse Pose Estimation
However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.
Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model
To address this problem, we here propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views.
SWIPENET: Object detection in noisy underwater images
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
Perceptual underwater image enhancement with deep learning and physical priors
Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration.
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects.
Deep Learning Based Brain Tumor Segmentation: A Survey
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
A Benchmark dataset for both underwater image enhancement and underwater object detection
To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset.
Low-light Image Enhancement Using the Cell Vibration Model
Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed.
Underwater object detection using Invert Multi-Class Adaboost with deep learning
In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.
AMIL: Adversarial Multi Instance Learning for Human Pose Estimation
The proposed adversarial residual multi-instance neural network that is based on pooling has been validated on two datasets for the human pose estimation task and successfully outperforms the other state-of-arts models.
A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
Multi-Source Transfer Learning for Non-Stationary Environments
To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie).
Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain
Achieving color constancy between and within images, i. e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition.
