Search Results for author:
Found 56 papers, 28 papers with code
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames.
Ranked #2 on
Multiview Gait Recognition
on OU-MVLP
GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild.
Ranked #1 on
Age-Invariant Face Recognition
on FG-NET
Learning Representation for Clustering via Prototype Scattering and Positive Sampling
The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness.
Ranked #2 on
Image Clustering
on ImageNet-10
Twin Contrastive Learning with Noisy Labels
In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification.
Ranked #19 on
Image Classification
on mini WebVision 1.0
Point, Segment and Count: A Generalized Framework for Object Counting
In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection.
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains.
PFA-GAN: Progressive Face Aging with Generative Adversarial Network
Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups.
Adaptive Nonlinear Latent Transformation for Conditional Face Editing
This paper proposes a novel adaptive nonlinear latent transformation for disentangled and conditional face editing, termed AdaTrans.
Prompt-In-Prompt Learning for Universal Image Restoration
Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.
Online Prototype Learning for Online Continual Learning
Online continual learning (CL) studies the problem of learning continuously from a single-pass data stream while adapting to new data and mitigating catastrophic forgetting.
Look globally, age locally: Face aging with an attention mechanism
Face aging is of great importance for cross-age recognition and entertainment-related applications.
AgeFlow: Conditional Age Progression and Regression with Normalizing Flows
Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively.
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process.
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images.
Cross-head Supervision for Crowd Counting with Noisy Annotations
To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision.
Semantic Latent Decomposition with Normalizing Flows for Face Editing
Specifically, SDFlow decomposes the original latent code into different irrelevant variables by jointly optimizing two components: (i) a semantic encoder to estimate semantic variables from input faces and (ii) a flow-based transformation module to map the latent code into a semantic-irrelevant variable in Gaussian distribution, conditioned on the learned semantic variables.
FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
Specifically, we first propose a frequency-band-aware artifact modeling network (FreeNet), which learns artifact-related frequency-band attention in Fourier domain for better modeling the globally distributed streak artifact on the sparse-view CT images.
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Moreover, we propose a simple yet effective Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets by learning the neighbor effect for each node.
SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition
In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction.
Learning to Distill Global Representation for Sparse-View CT
The reconstructed images, however, suffer from strong artifacts, greatly limiting their diagnostic value.
SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
To tackle this issue, we introduce a self-adaptive normalization network, termed SAN-Net, to achieve adaptive generalization on unseen sites for stroke lesion segmentation.
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.
Prompted Contextual Transformer for Incomplete-View CT Reconstruction
To enjoy the multi-setting synergy in a single model, we propose a novel Prompted Contextual Transformer (ProCT) for incomplete-view CT reconstruction.
Private Image Reconstruction from System Side Channels Using Generative Models
Given the ever-growing adoption of machine learning as a service (MLaaS), image analysis software on cloud platforms has been exploited by reconstructing private user images from system side channels.
HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction.
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties.
Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression
The latent vector preserves personalized face features and the age controls facial aging and rejuvenation.
Ranked #1 on
Age Estimation
on MORPH
The Automatic Identification of Butterfly Species
We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.
Distribution-dependent concentration inequalities for tighter generalization bounds
Concentration inequalities are indispensable tools for studying the generalization capacity of learning models.
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.
PaDNet: Pan-Density Crowd Counting
We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting.
Ordinal Distribution Regression for Gait-based Age Estimation
This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.
Precipitation Nowcasting with Star-Bridge Networks
Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community.
Towards a Precipitation Bias Corrector against Noise and Maldistribution
To the best of our knowledge, it is the first expert-free models for bias correction.
STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation
We thus propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC via the ST Feature-selective Mechanisms (SFM/TFM).
Convolutional Ordinal Regression Forest for Image Ordinal Estimation
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules
Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regression, showing better performance over traditional binary classification.
Meta ordinal weighting net for improving lung nodule classification
In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes.
RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditional GANs (cGANs)-based methods can learn various effects between any two age groups in a single model, but are insufficient to characterize some specific patterns due to completely shared convolutions filters; and 2) GANs-based methods can, by utilizing several models to learn effects independently, learn some specific patterns, however, they are cumbersome and require age label in advance.
Two Types of Mixed Orthogonal Frequency Division Multiplexing (X-OFDM) Waveform for Optical Wireless Communication
For the even sub-carriers in the frequency domain, the signal in the time domain after the IFFT is symmetric.
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications
In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced.
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration
In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment interactions.
Hard instance learning for quantum adiabatic prime factorization
Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography.
Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide.
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels
To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Crowd counting has important applications in public safety and pandemic control.
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
The performance of GNNs degrades as they become deeper due to the over-smoothing.
Ranked #8 on
Node Classification
on Squirrel
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance for both AIFR and FAS.
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer.
Binaural Rendering of Ambisonic Signals by Neural Networks
Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media.
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation.
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
Drawing on the capability of the contrastive language-image pre-training (CLIP) model to learn generalized visual representations from text annotations, in this paper, we propose CLIP-Lung, a textual knowledge-guided framework for lung nodule malignancy prediction.
ViP-Mixer: A Convolutional Mixer for Video Prediction
Video prediction aims to predict future frames from a video's previous content.
