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Found 20 papers, 15 papers with code
FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
Despite their impressive generative performance, latent diffusion model-based virtual try-on (VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and text.
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.
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.
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
On one hand, our contrastive emotion decoupling achieves decoupling learning via a contrastive decoupling loss to strengthen the separability of emotion-relevant features from corpus-specific ones.
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.
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.
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.
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
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.
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 #9 on
Node Classification
on Squirrel
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.
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.
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
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.
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.
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
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.
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.
Look globally, age locally: Face aging with an attention mechanism
Face aging is of great importance for cross-age recognition and entertainment-related applications.
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.
