Search Results for author:
Found 62 papers, 27 papers with code
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks such as graph classification and graph generation with a notable improvement.
An Empirical Study of Remote Sensing Pretraining
To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.
Change Detection
Change detection for remote sensing images
+1
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers
Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.
Ranked #12 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Multi-Tailed Vision Transformer for Efficient Inference
To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length.
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.
Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection
Deep learning for change detection is one of the current hot topics in the field of remote sensing.
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis
To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.
Ranked #1 on
Scene Text Recognition
on SVT
(using extra training data)
Expression is enough: Improving traffic signal control with advanced traffic state representation
Recently, finding fundamental properties for traffic state representation is more critical than complex algorithms for traffic signal control (TSC). In this paper, we (1) present a novel, flexible and straightforward method advanced max pressure (Advanced-MP), taking both running and queueing vehicles into consideration to decide whether to change current phase; (2) novelty design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); (3) develop an RL-based algorithm template Advanced-XLight, by combining ATS with current RL approaches and generate two RL algorithms, "Advanced-MPLight" and "Advanced-CoLight".
Self-Ensembling GAN for Cross-Domain Semantic Segmentation
In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN.
Binary Change Guided Hyperspectral Multiclass Change Detection
In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches.
Efficient Pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.
Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network
This method constructs the graph by the similarity relationship between cells and adopts GCN to analyze the neighbor embedding information of samples, which makes the similar cell closer to each other on the 2D scatter plot.
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
Graph pooling is essential in learning effective graph-level representations.
Robust Weight Perturbation for Adversarial Training
In this paper, we propose such a criterion, namely Loss Stationary Condition (LSC) for constrained perturbation.
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.
Ranked #19 on
Synthetic-to-Real Translation
on GTAV-to-Cityscapes Labels
(using extra training data)
Self-supervised Contrastive Learning for EEG-based Sleep Staging
In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.
DeepFake MNIST+: A DeepFake Facial Animation Dataset
It includes 10, 000 facial animation videos in ten different actions, which can spoof the recent liveness detectors.
Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images
With the goal of designing a quite deep architecture to obtain more precise CD results while simultaneously decreasing parameter numbers to improve efficiency, in this work, we present a very deep and efficient CD network, entitled EffCDNet.
Unsupervised Person Re-identification with Stochastic Training Strategy
State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.
I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.
Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification
At last, by combining the extracted spatial and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy classification.
Robust Self-Ensembling Network for Hyperspectral Image Classification
Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance.
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.
LocalDrop: A Hybrid Regularization for Deep Neural Networks
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.
A high-sensitivity fiber-coupled diamond magnetometer with surface coating
Nitrogen-vacancy quantum defects in diamond offer a promising platform for magnetometry because of their remarkable optical and spin properties.
Applied Physics Quantum Physics
Channel Augmented Joint Learning for Visible-Infrared Recognition
This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem.
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search
Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment
Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint.
Matrix Completion with Noise via Leveraged Sampling
This paper considers the recovery of a low-rank matrix, where some observed entries are sampled in a \emph{biased distribution} suitably dependent on \emph{leverage scores} of a matrix, and some observed entries are uniformly corrupted.
Hyperspectral Anomaly Change Detection Based on Auto-encoder
In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions.
Semantic Change Detection with Asymmetric Siamese Networks
Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.
Recurrent Feature Reasoning for Image Inpainting
To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.
Designing and Training of A Dual CNN for Image Denoising
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
Tchebycheff Procedure for Multi-task Text Classification
Multi-task Learning methods have achieved great progress in text classification.
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.
DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection
By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains.
Self-supervised Training of Graph Convolutional Networks
Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.
TextFuseNet: Scene Text Detection with Richer Fused Features
More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images
In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection.
OASIS: One-pass aligned Atlas Set for Image Segmentation
Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.
Multi-hop Convolutions on Weighted Graphs
To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.
Unified Multi-scale Feature Abstraction for Medical Image Segmentation
Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.
Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks
Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.
Exploring Representativeness and Informativeness for Active Learning
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Meanwhile, it is also hard to build a good model without diagnosing discriminative labels.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection.
Defect Detection from UAV Images based on Region-Based CNNs
In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.
Ranked #14 on
Unsupervised Domain Adaptation
on Market to Duke
Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker
Object tracking is a hot topic in computer vision.
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.
A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data
For the second target, a novel CNN-based universal framework is proposed to process the VHR satellite images and generate the land-use, urban density, and population distribution maps.
