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Found 32 papers, 9 papers with code
Variational Label Enhancement
Label distribution covers a certain number of labels, representing the degree to which each label describes the instance.
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
To address the task, we first analyze the theory of the multi-domain learning, which highlights that 1) mitigating the impact of domain gap and 2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels.
Language-Guided Face Animation by Recurrent StyleGAN-based Generator
Specifically, we propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames.
Progressive Purification for Instance-Dependent Partial Label Learning
Partial label learning (PLL) aims to train multi-class classifiers from instances with partial labels (PLs)-a PL for an instance is a set of candidate labels where a fixed but unknown candidate is the true label.
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label, and show that one can successfully learn a theoretically grounded multi-label classifier for the problem.
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
Decompositional Generation Process for Instance-Dependent Partial Label Learning
Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way.
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification
Different from the conventional data augmentation, the proposed domain-aware mix-normalization to enhance the diversity of features during training from the normalization view of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain.
Learning Hierarchical Graph Representation for Image Manipulation Detection
The objective of image manipulation detection is to identify and locate the manipulated regions in the images.
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework
A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic.
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores.
Ranked #1 on
Action Quality Assessment
on JIGSAWS
Instance-Dependent Partial Label Learning
In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature.
Learngene: From Open-World to Your Learning Task
Moreover, the learngene, i. e., the gene for learning initialization rules of the target model, is proposed to inherit the meta-knowledge from the collective model and reconstruct a lightweight individual model on the target task.
On the Robustness of Average Losses for Partial-Label Learning
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
Learning from Noisy Labels via Dynamic Loss Thresholding
During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds.
Compact Learning for Multi-Label Classification
In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance.
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
The effectiveness of our approach has been demonstrated on both facial age and attractiveness estimation tasks.
Ranked #1 on
Age Estimation
on ChaLearn 2016
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification
To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information.
Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
Multi-person pose estimation is an important but challenging problem in computer vision.
Age Estimation Using Expectation of Label Distribution Learning
Age estimation performance has been greatly improved by using convolutional neural network.
Multi-Label Learning with Label Enhancement
To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels.
Deep Label Distribution Learning with Label Ambiguity
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on
Head Pose Estimation
on BJUT-3D
Logistic Boosting Regression for Label Distribution Learning
In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint.
Label Distribution Learning
This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design.
Head Pose Estimation Based on Multivariate Label Distribution
Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms.
Multilabel Ranking with Inconsistent Rankers
The key idea is to learn a latent preference distribution for each instance.
