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Found 19 papers, 4 papers with code
Variational Label Enhancement
Label distribution covers a certain number of labels, representing the degree to which each label describes the instance.
Learngene: From Open-World to Your Learning Task
Although deep learning has made significant progress on fixed large-scale datasets, it typically encounters challenges regarding improperly detecting new/unseen classes in the open-world classification, over-parametrized, and overfitting small samples.
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classification problem, where a PL of an instance is a set of candidate labels such that a fixed but unknown candidate is the true label.
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
Our method achieves new state-of-the-art results using the single model with 36$\times$(6$\times$) fewer parameters and 2. 6$\times$(2. 1$\times$) faster inference speed on facial age (attractiveness) estimation.
Ranked #1 on
Attractiveness Estimation
on SCUT-FBP
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.
Multilabel Ranking with Inconsistent Rankers
The key idea is to learn a latent preference distribution for each instance.
Head Pose Estimation Based on Multivariate Label Distribution
Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms.
