Search Results for author: Xin Geng

Found 60 papers, 21 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

Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models

no code implementations25 Apr 2024 Shi-Yu Xia, Wenxuan Zhu, Xu Yang, Xin Geng

When initializing variable-sized models adapting for different resource constraints, SWS achieves better results while reducing around 20x parameters stored to initialize these models and around 10x pre-training costs, in contrast to the pre-training and fine-tuning approach.

DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization

no code implementations25 Mar 2024 Yunlong Tang, Yuxuan Wan, Lei Qi, Xin Geng

The Style Generation module refreshes all styles at every training epoch, while the Style Removal module eliminates variations in the encoder's output features caused by input styles.

Source-free Domain Generalization

A SAM-guided Two-stream Lightweight Model for Anomaly Detection

1 code implementation29 Feb 2024 Chenghao Li, Lei Qi, Xin Geng

In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM.

Decoder Unsupervised Anomaly Detection

CCA: Collaborative Competitive Agents for Image Editing

1 code implementation23 Jan 2024 Tiankai Hang, Shuyang Gu, Dong Chen, Xin Geng, Baining Guo

This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks.

Transferring Core Knowledge via Learngenes

no code implementations16 Jan 2024 Fu Feng, Jing Wang, Xin Geng

GTL trains a population of networks, selects superior learngenes by tournaments, performs learngene mutations, and passes the learngenes to next generations.

Transfer Learning

Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes

no code implementations15 Dec 2023 Shunxin Guo, Hongsong Wang, Xin Geng

Existing heterogeneous federated learning mainly focuses on skewing the label distribution across clients.

Federated Learning Privacy Preserving

Building Variable-sized Models via Learngene Pool

no code implementations10 Dec 2023 Boyu Shi, Shiyu Xia, Xu Yang, Haokun Chen, Zhiqiang Kou, Xin Geng

To overcome these challenges, motivated by the recently proposed Learngene framework, we propose a novel method called Learngene Pool.

Transformer as Linear Expansion of Learngene

1 code implementation9 Dec 2023 Shiyu Xia, Miaosen Zhang, Xu Yang, Ruiming Chen, Haokun Chen, Xin Geng

Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19x parameters stored to initialize these models and around 5x pre-training costs, in contrast to the pre-training and fine-tuning approach.

Manipulating the Label Space for In-Context Classification

no code implementations1 Dec 2023 Haokun Chen, Xu Yang, Yuhang Huang, Zihan Wu, Jing Wang, Xin Geng

Specifically, using our approach on ImageNet, we increase accuracy from 74. 70\% in a 4-shot setting to 76. 21\% with just 2 shots.

Classification Contrastive Learning +2

Scalable Label Distribution Learning for Multi-Label Classification

1 code implementation28 Nov 2023 Xingyu Zhao, Yuexuan An, Lei Qi, Xin Geng

Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.

Classification Multi-Label Classification

DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory

no code implementations22 Nov 2023 Lei Qi, Peng Dong, Tan Xiong, Hui Xue, Xin Geng

In this paper, we aim to solve the single-domain generalizable object detection task in urban scenarios, meaning that a model trained on images from one weather condition should be able to perform well on images from any other weather conditions.

Autonomous Driving object-detection +1

Can Class-Priors Help Single-Positive Multi-Label Learning?

no code implementations25 Sep 2023 Biao Liu, Jie Wang, Ning Xu, Xin Geng

Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label.

Multi-Label Learning

Robust Representation Learning for Unreliable Partial Label Learning

no code implementations31 Aug 2023 Yu Shi, Dong-Dong Wu, Xin Geng, Min-Ling Zhang

This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods.

Contrastive Learning Partial Label Learning +2

A Novel Cross-Perturbation for Single Domain Generalization

no code implementations2 Aug 2023 Dongjia Zhao, Lei Qi, Xiao Shi, Yinghuan Shi, Xin Geng

Horizontally, it applies image-level and feature-level perturbations to enhance the diversity of the training data, mitigating the issue of limited diversity in single-source domains.

Domain Generalization

Variational Label-Correlation Enhancement for Congestion Prediction

no code implementations1 Aug 2023 Biao Liu, Congyu Qiao, Ning Xu, Xin Geng, Ziran Zhu, Jun Yang

In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, {\ours}, i. e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids.

Variational Inference

Generalizable Metric Network for Cross-domain Person Re-identification

no code implementations21 Jun 2023 Lei Qi, Ziang Liu, Yinghuan Shi, Xin Geng

Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity.

Domain Generalization Person Re-Identification

Genes in Intelligent Agents

1 code implementation17 Jun 2023 Fu Feng, Jing Wang, Xu Yang, Xin Geng

Inspired by the biological intelligence, artificial intelligence (AI) has devoted to building the machine intelligence.

reinforcement-learning Reinforcement Learning (RL)

Epistemic Graph: A Plug-And-Play Module For Hybrid Representation Learning

no code implementations30 May 2023 Jin Yuan, Yang Zhang, Yangzhou Du, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

In this paper, a novel Epistemic Graph Layer (EGLayer) is introduced to enable hybrid learning, enhancing the exchange of information between deep features and a structured knowledge graph.

Few-Shot Learning Knowledge Graphs +1

Exploring Diverse In-Context Configurations for Image Captioning

1 code implementation NeurIPS 2023 Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, Xin Geng

After discovering that Language Models (LMs) can be good in-context few-shot learners, numerous strategies have been proposed to optimize in-context sequence configurations.

Image Captioning In-Context Learning

Towards Effective Visual Representations for Partial-Label Learning

1 code implementation CVPR 2023 Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng

Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities.

Contrastive Learning Image Classification +3

Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

no code implementations3 May 2023 Qiufeng Wang, Xu Yang, Shuxia Lin, Jing Wang, Xin Geng

(i) Accumulating: the knowledge is accumulated during the continuous learning of an ancestry model.

Patch-aware Batch Normalization for Improving Cross-domain Robustness

no code implementations6 Apr 2023 Lei Qi, Dongjia Zhao, Yinghuan Shi, Xin Geng

By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters.

Data Augmentation object-detection +3

Data Augmentation For Label Enhancement

no code implementations21 Mar 2023 Zhiqiang Kou, Yuheng Jia, Jing Wang, Boyu Shi, Xin Geng

Existing LE approach have the following problems: (\textbf{i}) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (\textbf{ii}) They ignore feature redundancy and use the collected features directly.

Data Augmentation Dimensionality Reduction

Efficient Diffusion Training via Min-SNR Weighting Strategy

2 code implementations ICCV 2023 Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, Baining Guo

Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.

Denoising Image Generation +2

Inaccurate Label Distribution Learning

no code implementations25 Feb 2023 Zhiqiang Kou, Yuheng Jia, Jing Wang, Xin Geng

The previous LDL methods all assumed the LDs of the training instances are accurate.

Unreliable Partial Label Learning with Recursive Separation

no code implementations20 Feb 2023 Yu Shi, Ning Xu, Hua Yuan, Xin Geng

Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set.

Partial Label Learning Weakly-supervised Learning

Language-Guided Face Animation by Recurrent StyleGAN-based Generator

1 code implementation11 Aug 2022 Tiankai Hang, Huan Yang, Bei Liu, Jianlong Fu, Xin Geng, Baining Guo

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.

Image Manipulation

MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization

no code implementations11 Aug 2022 Lei Qi, Hongpeng Yang, Yinghuan Shi, Xin Geng

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.

Domain Generalization Multi-Task Learning +2

Progressive Purification for Instance-Dependent Partial Label Learning

no code implementations2 Jun 2022 Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng

Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct.

Partial Label Learning

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

1 code implementation1 Jun 2022 Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang

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.

Multi-Label Learning

Label Distribution Learning for Generalizable Multi-source Person Re-identification

no code implementations12 Apr 2022 Lei Qi, Jiaying Shen, Jiaqi Liu, Yinghuan Shi, Xin Geng

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.

Person Re-Identification

Decompositional Generation Process for Instance-Dependent Partial Label Learning

1 code implementation8 Apr 2022 Congyu Qiao, Ning Xu, Xin Geng

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.

Partial Label Learning Weakly-supervised Learning

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

1 code implementation8 Apr 2022 Jin Yuan, Feng Hou, Yangzhou Du, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

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.

Domain Adaptation Self-Supervised Learning +1

Graph Attention Transformer Network for Multi-Label Image Classification

1 code implementation8 Mar 2022 Jin Yuan, Shikai Chen, Yao Zhang, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.

Classification Graph Attention +2

A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification

no code implementations24 Jan 2022 Lei Qi, Lei Wang, Yinghuan Shi, Xin Geng

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.

Data Augmentation Person Re-Identification

Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework

1 code implementation30 Nov 2021 Lei Qi, Jiaqi Liu, Lei Wang, Yinghuan Shi, Xin Geng

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.

Domain Generalization Person Re-Identification +1

Auto-Encoding Score Distribution Regression for Action Quality Assessment

2 code implementations22 Nov 2021 Boyu Zhang, Jiayuan Chen, Yinfei Xu, HUI ZHANG, Xu Yang, Xin Geng

Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores.

Action Quality Assessment regression

Instance-Dependent Partial Label Learning

1 code implementation NeurIPS 2021 Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang

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.

Partial Label Learning Weakly-supervised Learning

Learngene: From Open-World to Your Learning Task

1 code implementation12 Jun 2021 Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu

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

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

Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).

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

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 +1

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

2 code implementations6 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 +3

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.

Clustering 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

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