Search Results for author:
Found 48 papers, 24 papers with code
UMBRAE: Unified Multimodal Decoding of Brain Signals
We address prevailing challenges of the brain-powered research, departing from the observation that the literature hardly recover accurate spatial information and require subject-specific models.
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCN) have recently been studied to exploit the graph topology of the human body for skeleton-based action recognition.
Ranked #1 on
Skeleton Based Action Recognition
on NTU RGB+D 120
High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification
Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images.
Spatial-Contextual Discrepancy Information Compensation for GAN Inversion
SDIC follows a "compensate-and-edit" paradigm and successfully bridges the gap in image details between the original image and the reconstructed/edited image.
Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models
To address this issue, we propose a method called StitchDiffusion.
DREAM: Visual Decoding from Reversing Human Visual System
In this work we present DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system.
A Survey on Deep Generative 3D-aware Image Synthesis
Recent years have seen remarkable progress in deep learning powered visual content creation.
Modelling Latent Dynamics of StyleGAN using Neural ODEs
The entire sequence is seen as discrete-time observations of a continuous trajectory of the initial latent code, by considering each latent code as a moving particle and the latent space as a high-dimensional dynamic system.
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition
Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods.
cross-domain few-shot learning
Facial Expression Recognition
+1
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes
In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes.
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework.
cross-domain few-shot learning
Facial Expression Recognition
+1
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner.
Ranked #45 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Dimension Reduction for Data with Heterogeneous Missingness
Dimension reduction plays a pivotal role in analysing high-dimensional data.
Group Fisher Pruning for Practical Network Compression
Our method can be used to prune any structures including those with coupled channels.
Ranked #4 on
Network Pruning
on ImageNet
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data
First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data.
Ranked #3 on
Facial Attribute Classification
on LFWA
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images.
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.
Towards Open-World Text-Guided Face Image Generation and Manipulation
To be specific, we propose a brand new paradigm of text-guided image generation and manipulation based on the superior characteristics of a pretrained GAN model.
Ranked #5 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
GAN Inversion: A Survey
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator.
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions.
Ranked #6 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
Metric learning aims to learn a distance measure that can benefit distance-based methods such as the nearest neighbour (NN) classifier.
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures.
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
Controllable Continuous Gaze Redirection
The encoder maps images into a well-disentangled and hierarchically-organized latent space.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
Towards Certified Robustness of Distance Metric Learning
Many existing methods consider maximizing or at least constraining a distance margin in the feature space that separates similar and dissimilar pairs of instances to guarantee their generalization ability.
A Concise Review of Recent Few-shot Meta-learning Methods
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge.
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images.
XSepConv: Extremely Separated Convolution
Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently.
Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification
Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training.
Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions
Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions.
Unsupervised Multi-Domain Multimodal Image-to-Image Translation with Explicit Domain-Constrained Disentanglement
Image-to-image translation has drawn great attention during the past few years.
Domain Fingerprints for No-reference Image Quality Assessment
Human fingerprints are detailed and nearly unique markers of human identity.
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Human-Like Sketches
Image generation has received increasing attention because of its wide application in security and entertainment.
Discriminant analysis based on projection onto generalized difference subspace
To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion.
An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion
Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB.
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution.
Ranked #14 on
Image Super-Resolution
on Set14 - 3x upscaling
Constrained Mutual Convex Cone Method for Image Set Based Recognition
Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, similar to the Fisher discriminant analysis.
Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images
To mitigate these limitations and promote further research on hand pose estimation from stereo images, we propose a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization.
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.
SEA: A Combined Model for Heat Demand Prediction
ENN and ARIMA are used to predict seasonal and trend components, respectively.
BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.
Learning Local Metrics and Influential Regions for Classification
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data.
Metric Learning via Maximizing the Lipschitz Margin Ratio
In this paper, we propose the Lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio.
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable decorrelation.
