Search Results for author:
Found 100 papers, 36 papers with code
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).
Ranked #1 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
DenseFuse: A Fusion Approach to Infrared and Visible Images
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem.
Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
Infrared and Visible Image Fusion using a Deep Learning Framework
Then the base parts are fused by weighted-averaging.
Peeking into occluded joints: A novel framework for crowd pose estimation
Although occlusion widely exists in nature and remains a fundamental challenge for pose estimation, existing heatmap-based approaches suffer serious degradation on occlusions.
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand.
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold.
Infrared and Visible Image Fusion with ResNet and zero-phase component analysis
Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken.
NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models
In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features.
Infrared and visible image fusion using Latent Low-Rank Representation
Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information.
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images
In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model.
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking.
Ranked #1 on
Visual Object Tracking
on VOT2017
A Dual-branch Network for Infrared and Visible Image Fusion
Deep learning is a rapidly developing approach in the field of infrared and visible image fusion.
Generative Adversarial Network
Infrared And Visible Image Fusion
+1
MDLatLRR: A novel decomposition method for infrared and visible image fusion
We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts.
NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
To deal with this issue, we advocate a novel lexical enhancement method, InterFormer, that effectively reduces the amount of computational and memory costs by constructing non-flat lattices.
Ranked #9 on
Chinese Named Entity Recognition
on Resume NER
Chinese Named Entity Recognition
named-entity-recognition
+2
Multi-focus Noisy Image Fusion using Low-Rank Representation
Multi-focus noisy image fusion represents an important task in the field of image fusion which generates a single, clear and focused image from all source images.
Multi-focus Image Fusion using dictionary learning and Low-Rank Representation
In this paper, we propose a novel multi-focus image fusion method based on dictionary learning and LRR to get a better performance in both global and local structure.
Self-grouping Convolutional Neural Networks
To tackle this issue, we propose a novel method of designing self-grouping convolutional neural networks, called SG-CNN, in which the filters of each convolutional layer group themselves based on the similarity of their importance vectors.
SDA-$x$Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation
To tackle this issue, we first introduce a new attention dimension, i. e., depth, in addition to existing attention dimensions such as channel, spatial, and branch, and present a novel selective depth attention network to symmetrically handle multi-scale objects in various vision tasks.
Differentiable Neural Architecture Learning for Efficient Neural Network Design
To address this computational complexity issue, we introduce a novel \emph{architecture parameterisation} based on scaled sigmoid function, and propose a general \emph{Differentiable Neural Architecture Learning} (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks.
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset.
Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking
The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation.
Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning
First, a low-rank representation is introduced to handle the possible contamination of the training as well as test data.
Temporal Aggregation for Adaptive RGBT Tracking
Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance. As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics contained in two modalities.
TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images.
Fisher Discriminative Least Squares Regression for Image Classification
On one hand, the Fisher criterion improves the intra-class compactness of the relaxed labels during relaxation learning.
Generative-based Fusion Mechanism for Multi-Modal Tracking
In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking.
Riemannian Local Mechanism for SPD Neural Networks
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas.
Class-specific residual constraint non-negative representation for pattern classification
Representation based classification method (RBCM) remains one of the hottest topics in the community of pattern recognition, and the recently proposed non-negative representation based classification (NRC) achieved impressive recognition results in various classification tasks.
More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representation
We consider a family of structural descriptors for visual data, namely covariance descriptors (CovDs) that lie on a non-linear symmetric positive definite (SPD) manifold, a special type of Riemannian manifolds.
Multiplication fusion of sparse and collaborative-competitive representation for image classification
Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively.
Affine Non-negative Collaborative Representation Based Pattern Classification
Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces.
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications
Based on the predicted P2P, four effective strategies are introduced to show the BDA performance.
FusionBooster: A Unified Image Fusion Boosting Paradigm
We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for the fusion task.
Component SPD Matrices: A lower-dimensional discriminative data descriptor for image set classification
In the domain of pattern recognition, using the SPD (Symmetric Positive Definite) matrices to represent data and taking the metrics of resulting Riemannian manifold into account have been widely used for the task of image set classification.
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel.
Multiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riemannian manifold.
A Simple Riemannian Manifold Network for Image Set Classification
The proposed RieMNet and DRieMNet are evaluated on three tasks: video-based face recognition, set-based object categorization, and set-based cell identification.
L1-(2D)2PCANet: A Deep Learning Network for Face Recognition
In our network, the role of L1-(2D)2PCA is to learn the filters of multiple convolution layers.
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild
To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans.
A Unified Tensor-based Active Appearance Face Model
To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces.
Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild
The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation.
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces.
Ranked #18 on
Face Alignment
on AFLW-19
Dictionary Integration using 3D Morphable Face Models for Pose-invariant Collaborative-representation-based Classification
The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification.
Random Drift Particle Swarm Optimization
The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field.
Semi-supervised Hashing for Semi-Paired Cross-View Retrieval
Recently, hashing techniques have gained importance in large-scale retrieval tasks because of their retrieval speed.
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
The core of the method is a new discriminant function for metric learning and dimensionality reduction.
Landmark Weighting for 3DMM Shape Fitting
In this paper we show that landmark weighting is instrumental to improve the accuracy of shape reconstruction and propose a novel 3D Morphable Model Fitting method.
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
Many hashing methods based on a single view have been extensively studied for information retrieval.
Pose Invariant 3D Face Reconstruction
Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction.
Non-negative representation based discriminative dictionary learning for face recognition
In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification.
Low-Rank Discriminative Least Squares Regression for Image Classification
To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification.
Discriminative Supervised Hashing for Cross-Modal similarity Search
With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search.
Unsupervised Multi-modal Hashing for Cross-modal retrieval
With the advantage of low storage cost and high efficiency, hashing learning has received much attention in the domain of Big Data.
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving
Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality.
Transition Subspace Learning based Least Squares Regression for Image Classification
Only learning one projection matrix from original samples to the corresponding binary labels is too strict and will consequentlly lose some intrinsic geometric structures of data.
2D Attentional Irregular Scene Text Recognizer
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers.
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously.
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning
To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose.
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
In order to capture the rich spatiotemporal information and utilize features more effectively, we introduce a spatial residual layer and a dense connection block enhanced spatial temporal graph convolutional network.
Ranked #47 on
Skeleton Based Action Recognition
on NTU RGB+D
Locality Constraint Dictionary Learning with Support Vector for Pattern Classification
In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary.
Learning a Representation with the Block-Diagonal Structure for Pattern Classification
To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition.
An Accelerated Correlation Filter Tracker
Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features.
Low-rank representations with incoherent dictionary for face recognition
Face recognition remains a hot topic in computer vision, and it is challenging to tackle the problem that both the training and testing images are corrupted.
Multi-focus Image Fusion Based on Similarity Characteristics
For three source images, a joint region segmentation method based on segmentation of two images is used to obtain the final segmentation result.
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix.
Collaborative representation-based robust face recognition by discriminative low-rank representation
Collaborative representation based classification (CRC) method is exploited in our proposed method which has closed-form solution.
Research on Clustering Performance of Sparse Subspace Clustering
Recently, sparse subspace clustering has been a valid tool to deal with high-dimensional data.
A Compared Study Between Some Subspace Based Algorithms
The technology of face recognition has made some progress in recent years.
2DR1-PCA and 2DL1-PCA: two variant 2DPCA algorithms based on none L2 norm
In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition.
Adaptive Distraction Context Aware Tracking Based on Correlation Filter
In the response map obtained for the previous frame by the CF algorithm, we adaptively find the image blocks that are similar to the target and use them as negative samples.
Robust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms
With the guaranteed discrimination and efficiency of spatial appearance model, Discriminative Correlation Filters (DCF-) based tracking methods have achieved outstanding performance recently.
Face Verification via learning the kernel matrix
The method CSKDA needs to choose a proper kernel function through many experiments, while the new method could learn the kernel from data automatically which could save a lot of time and have the robust performance.
Improved dual channel pulse coupled neural network and its application to multi-focus image fusion
This paper presents an improved dual channel pulse coupled neural network (IDC-PCNN) model for image fusion.
Learning efficient structured dictionary for image classification
Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification.
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking
To this end, we propose a failure-aware system, realised by a Quality Prediction Network (QPN), based on convolutional and LSTM modules in the decision stage, enabling online reporting of potential tracking failures.
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification
This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes.
Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion
In this paper, we apply the image decomposition network to the image fusion task.
MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval
To this end, we develop a novel Multiple hash cOdes jOint learNing method (MOON) for cross-media retrieval.
Video Is Graph: Structured Graph Module for Video Action Recognition
In this paper, we first propose to transform a video sequence into a graph to obtain direct long-term dependencies among temporal frames.
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution
Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner.
Res2NetFuse: A Fusion Method for Infrared and Visible Images
The Res2Net-based encoder is used to extract multi-scale features of source images, the paper introducing a new training strategy for training a Res2Net-based encoder that uses only a single image.
A Survey for Deep RGBT Tracking
This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
Face recognition via compact second order image gradient orientations
The second order image gradient orientations (SOIGO) can mitigate the adverse effect of noises in face images.
Visual Object Tracking on Multi-modal RGB-D Videos: A Review
The development of visual object tracking has continued for decades.
Collaborative Representation for SPD Matrices with Application to Image-Set Classification
Recent advances illustrate that how to effectively model these nonlinear variational information and learn invariant representations is an open challenge in the community of computer vision and pattern recognition To this end, we try to design a new algorithm to handle this problem.
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance.
Generative Adversarial Network
Infrared And Visible Image Fusion
Unsupervised Image Fusion Method based on Feature Mutual Mapping
We propose a feature mutual mapping fusion module and dual-branch multi-scale autoencoder.
Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model
The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states).
A Joint Convolution Auto-encoder Network for Infrared and Visible Image Fusion
Background: Leaning redundant and complementary relationships is a critical step in the human visual system.
Discriminative Supervised Subspace Learning for Cross-modal Retrieval
Thirdly, we introduce a similarity preservation term, thus our model can compensate for the shortcomings of insufficient use of discriminative data and better preserve the semantically structural information within each modality.
Low-rank features based double transformation matrices learning for image classification
However, methods based on this technique ignore the pressure on a single transformation matrix due to the complex information contained in the data.
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification
Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold.
A Medical Image Fusion Method based on MDLatLRRv2
Since MDLatLRR only considers detailed parts (salient features) of input images extracted by latent low-rank representation (LatLRR), it doesn't use base parts (principal features) extracted by LatLRR effectively.
Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN
In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes.
Adaptive Riemannian Metrics on SPD Manifolds
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data.
Analyzing and controlling diversity in quantum-behaved particle swarm optimization
Then, the correlations between the two types of diversities and the search performance are tested and analyzed on several benchmark functions, and the distance-to-average-point diversity is showed to have stronger association with the search performance during the evolving processes.
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
Contrastive learning has achieved great success in skeleton-based action recognition.
Feature Space Renormalization for Semi-supervised Learning
The common framework among recent approaches is to train the model on a large amount of unlabelled data with consistency regularization to constrain the model predictions to be invariant to input perturbation.
Riemannian Self-Attention Mechanism for SPD Networks
Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the data adequately on a curved Riemannian manifold, i. e., SPD manifold.
