Search Results for author: Xin Geng

Found 19 papers, 4 papers with code

Variational Label Enhancement

no code implementations ICML 2020 Ning Xu, Yun-Peng Liu, Jun Shu, Xin Geng

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance.

Multi-Label Learning Variational Inference

Learngene: From Open-World to Your Learning Task

no code implementations12 Jun 2021 Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu

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

no code implementations11 Jun 2021 Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama

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.

Partial Label Learning Weakly Supervised Classification

Learning from Noisy Labels via Dynamic Loss Thresholding

no code implementations1 Apr 2021 Hao Yang, Youzhi Jin, Ziyin Li, Deng-Bao Wang, Lei Miao, Xin Geng, Min-Ling Zhang

During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds.

Compact Learning for Multi-Label Classification

no code implementations18 Sep 2020 Jiaqi Lv, Tianran Wu, Chenglun Peng, Yun-Peng Liu, Ning Xu, Xin Geng

In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance.

Classification Dimensionality Reduction +3

Provably Consistent Partial-Label Learning

no code implementations NeurIPS 2020 Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.

Multi-class Classification Partial Label Learning

Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation

1 code implementation3 Jul 2020 Bin-Bin Gao, Xin-Xin Liu, Hong-Yu Zhou, Jianxin Wu, Xin Geng

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.

Age Estimation Attractiveness Estimation

Progressive Identification of True Labels for Partial-Label Learning

1 code implementation ICML 2020 Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama

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.

Partial Label Learning Stochastic Optimization

Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification

no code implementations2 Aug 2019 Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, Xin Geng, Yang Gao

To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.

Person Re-Identification

A Context-and-Spatial Aware Network for Multi-Person Pose Estimation

no code implementations14 May 2019 Dongdong Yu, Kai Su, Xin Geng, Changhu Wang

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

Age Estimation Using Expectation of Label Distribution Learning

1 code implementation13 Jul 2018 Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng

Age estimation performance has been greatly improved by using convolutional neural network.

Age Estimation Face Recognition +1

Multi-Label Learning with Label Enhancement

no code implementations26 Jun 2017 Ruifeng Shao, Ning Xu, Xin Geng

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.

Multi-Label Learning

Deep Label Distribution Learning with Label Ambiguity

1 code implementation6 Nov 2016 Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, Xin Geng

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.

Age Estimation Classification +4

Logistic Boosting Regression for Label Distribution Learning

no code implementations CVPR 2016 Chao Xing, Xin Geng, Hui Xue

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.

Age Estimation Facial Expression Recognition +1

Label Distribution Learning

no code implementations26 Aug 2014 Xin Geng

This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design.

Multi-Label Learning

Multilabel Ranking with Inconsistent Rankers

no code implementations CVPR 2014 Xin Geng, Longrun Luo

The key idea is to learn a latent preference distribution for each instance.

Head Pose Estimation Based on Multivariate Label Distribution

no code implementations CVPR 2014 Xin Geng, Yu Xia

Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms.

Head Pose Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.