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Found 79 papers, 16 papers with code
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity.
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem.
Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations
Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image.
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification
Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade.
Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
This paper develops a novel iterative framework for subspace clustering in a learned discriminative feature domain.
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented.
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
In this paper, we propose an accelerated first-order method for geodesically convex optimization, which is the generalization of the standard Nesterov's accelerated method from Euclidean space to nonlinear Riemannian space.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD).
Polarimetric Convolutional Network for PolSAR Image Classification
Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information.
Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images
More recently, a two-stage detector Faster R-CNN is proposed and demonstrated to be a promising tool for object detection in optical remote sensing images, while the sparse and dense characteristic of objects in remote sensing images is complexity.
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification.
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick.
Efficient Computation of Quantized Neural Networks by {−1, +1} Encoding Decomposition
Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.
Parameter-Free Spatial Attention Network for Person Re-Identification
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #20 on
Person Re-Identification
on DukeMTMC-reID
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL).
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs).
Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
However, their interpretation faces some challenges, e. g., deficiency of labeled data, inadequate utilization of data information and so on.
PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine
In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine.
Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
Change detection is a quite challenging task due to the imbalance between unchanged and changed class.
SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
In this letter, we proposed a spatial metric learning method to obtain a difference image more robust to the speckle by learning a metric from a set of constraint pairs.
A Survey of Deep Learning-based Object Detection
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.
signADAM: Learning Confidences for Deep Neural Networks
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks.
Hierarchical Feature-Aware Tracking
Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated.
PolSF: PolSAR image dataset on San Francisco
In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area.
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification
To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed.
Ranked #7 on
Hyperspectral Image Classification
on Indian Pines
Few-Shot Image Classification
Generative Adversarial Network
+2
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset.
Counterfactual Detection meets Transfer Learning
We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements.
A Light-Weighted Convolutional Neural Network for Bitemporal SAR Image Change Detection
We verify our light-weighted neural network on four sets of bitemporal SAR images.
MultiResolution Attention Extractor for Small Object Detection
Small objects are difficult to detect because of their low resolution and small size.
C-CNN: Contourlet Convolutional Neural Networks
Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture.
Contralaterally Enhanced Networks for Thoracic Disease Detection
Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
Boosting Gradient for White-Box Adversarial Attacks
Existing white-box attack algorithms can generate powerful adversarial examples.
New Generation Deep Learning For Video Object Dection:A Survery
Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used.
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips.
MWQ: Multiscale Wavelet Quantized Neural Networks
Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method.
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
Second, the group spatial attention and group spectral attention modules are proposed to extract image features.
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
When the precision of quantization is adjusted, it is necessary to fine-tune the quantized model or minimize the quantization noise, which brings inconvenience in practical applications.
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation
Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
We propose a balanced coarsening scheme for multilevel hypergraph partitioning.
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage.
Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition
Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space.
Facial Expression Recognition
Facial Expression Recognition (FER)
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).
Unsupervised Outlier Detection using Memory and Contrastive Learning
Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.
Multi-Scale Progressive Attention Network for Video Question Answering
Under the question{'}s guidance of progressive attention, we realize the fusion of all-scale video features.
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.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition.
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
We convert a lightweight FCOSR model to TensorRT format, which achieves 73. 93 mAP on DOTA1. 0 at a speed of 10. 68 FPS on Jetson Xavier NX with single scale.
MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images
Ship detection in aerial images remains an active yet challenging task due to arbitrary object orientation and complex background from a bird's-eye perspective.
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition
Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks.
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Three image tasks and two video tasks of computer vision are investigated.
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network
In the proposed method, two parts are constructed based on facial local and non-local information respectively, where an ensemble of multiple local networks are proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region.
Facial Expression Recognition
Facial Expression Recognition (FER)
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes.
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.
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification
Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set.
Delving into Semantic Scale Imbalance
Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain.
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution.
SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images
Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper.
Curvature-Balanced Feature Manifold Learning for Long-Tailed Classification
In this work, we systematically propose a series of geometric measurements for perceptual manifolds in deep neural networks, and then explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds.
Ranked #19 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking
To this end, a multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking (EMO-DIR) is proposed in this paper.
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing
This paper proposes a novel knowledge distillation algorithm based on dynamic entropy correction to reduce the gap by adjusting the student instead of the teacher.
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).
Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids
Drones have been widely used in many areas of our daily lives.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods.
Changes-Aware Transformer: Learning Generalized Changes Representation
After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference.
Data-Centric Long-Tailed Image Recognition
In the context of the long-tail scenario, models exhibit a strong demand for high-quality data.
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.
A match made in consistency heaven: when large language models meet evolutionary algorithms
Pre-trained large language models (LLMs) have powerful capabilities for generating creative natural text.
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
In this work, we propose to leverage the geometric information of the feature distribution of the well-represented head class to guide the model to learn the underlying distribution of the tail class.
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
To eliminate the reliance on such priors, we propose a novel Structure-aware Mixup and Invariance Learning framework (SA-MixNet) for weakly supervised road extraction that improves the model invariance in a data-driven manner.
Supervised Edge Attention Network for Accurate Image Instance Segmentation
In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask.
