Search Results for author: Jihua Zhu

Found 40 papers, 10 papers with code

Evaluation and Analysis of Hallucination in Large Vision-Language Models

no code implementations29 Aug 2023 Junyang Wang, Yiyang Zhou, Guohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Jihua Zhu, Jitao Sang, Haoyu Tang

In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework.

Overlap Bias Matching is Necessary for Point Cloud Registration

no code implementations18 Aug 2023 Pengcheng Shi, Jie Zhang, Haozhe Cheng, Junyang Wang, Yiyang Zhou, Chenlin Zhao, Jihua Zhu

Specifically, we propose a plug-and-play Overlap Bias Matching Module (OBMM) comprising two integral components, overlap sampling module and bias prediction module.

Point Cloud Registration

Contrastive Label Enhancement

no code implementations16 May 2023 Yifei Wang, Yiyang Zhou, Jihua Zhu, Xinyuan Liu, Wenbiao Yan, Zhiqiang Tian

Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.

Contrastive Learning

DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion

no code implementations16 May 2023 Pengcheng Shi, Haozhe Cheng, Xu Han, Yiyang Zhou, Jihua Zhu

To tackle these challenges, we propose an information interaction-based generative network for point cloud completion ($\mathbf{DualGenerator}$).

Point Cloud Completion

Label Information Bottleneck for Label Enhancement

1 code implementation CVPR 2023 Qinghai Zheng, Jihua Zhu, Haoyu Tang

In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE.

Semantically Consistent Multi-view Representation Learning

no code implementations8 Mar 2023 Yiyang Zhou, Qinghai Zheng, Shunshun Bai, Jihua Zhu

In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner.

Contrastive Learning Representation Learning

Multi-view Semantic Consistency based Information Bottleneck for Clustering

no code implementations28 Feb 2023 Wenbiao Yan, Jihua Zhu, Yiyang Zhou, Yifei Wang, Qinghai Zheng

In this way, the learned semantic consistency from multi-view data can improve the information bottleneck to more exactly distinguish the consistent information and learn a unified feature representation with more discriminative consistent information for clustering.


MCoCo: Multi-level Consistency Collaborative Multi-view Clustering

no code implementations26 Feb 2023 Yiyang Zhou, Qinghai Zheng, Wenbiao Yan, Yifei Wang, Pengcheng Shi, Jihua Zhu

Further, we designed a multi-level consistency collaboration strategy, which utilizes the consistent information of semantic space as a self-supervised signal to collaborate with the cluster assignments in feature space.

Clustering Contrastive Learning +2

3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation

no code implementations2 Dec 2022 Zutao Jiang, Guansong Lu, Xiaodan Liang, Jihua Zhu, Wei zhang, Xiaojun Chang, Hang Xu

Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module.

Contrastive Learning SSIM

Dynamic Slimmable Denoising Network

no code implementations17 Oct 2021 Zutao Jiang, Changlin Li, Xiaojun Chang, Jihua Zhu, Yi Yang

Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images.

Fairness Image Denoising

AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person

no code implementations9 Aug 2021 Xinsheng Wang, Qicong Xie, Jihua Zhu, Lei Xie, Scharenborg

In this paper, we present an automatic method to generate synchronized speech and talking-head videos on the basis of text and a single face image of an arbitrary person as input.

Talking Head Generation

3DMNDT:3D multi-view registration method based on the normal distributions transform

no code implementations20 Mar 2021 Jihua Zhu, Di Wang, Jiaxi Mu, Huimin Lu, Zhiqiang Tian, Zhongyu Li

Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration.


Effective multi-view registration of point sets based on student's t mixture model

1 code implementation13 Dec 2020 Yanlin Ma, Jihua Zhu, Zhongyu Li, Zhiqiang Tian, Yaochen Li

What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers.

Show and Speak: Directly Synthesize Spoken Description of Images

1 code implementation23 Oct 2020 Xinsheng Wang, Siyuan Feng, Jihua Zhu, Mark Hasegawa-Johnson, Odette Scharenborg

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes.

Multi-view Subspace Clustering Networks with Local and Global Graph Information

1 code implementation19 Oct 2020 Qinghai Zheng, Jihua Zhu, Yuanyuan Ma, Zhongyu Li, Zhiqiang Tian

Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.

Clustering Multi-view Subspace Clustering

Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering

no code implementations19 Oct 2020 Qinghai Zheng, Yu Zhang, Jihua Zhu, Zhongyu Li, Haoyu Tang, Shuangxun Ma

It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint.

Clustering Multi-view Subspace Clustering +1

Multi-view Hierarchical Clustering

no code implementations15 Oct 2020 Qinghai Zheng, Jihua Zhu, Shuangxun Ma

This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data.


Frame-wise Cross-modal Matching for Video Moment Retrieval

1 code implementation22 Sep 2020 Haoyu Tang, Jihua Zhu, Meng Liu, Member, IEEE, Zan Gao, Zhiyong Cheng

Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.

Boundary Detection Moment Retrieval +1

Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics

no code implementations21 Aug 2020 Dou Xu, Chang Cai, Chaowei Fang, Bin Kong, Jihua Zhu, Zhongyu Li

To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels.

Contrastive Learning Histopathological Image Classification +3

Bidirectional Loss Function for Label Enhancement and Distribution Learning

no code implementations7 Jul 2020 Xinyuan Liu, Jihua Zhu, Qinghai Zheng, Zhongyu Li, Ruixin Liu, Jun Wang

More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.

Multi-Label Learning

S2IGAN: Speech-to-Image Generation via Adversarial Learning

2 code implementations14 May 2020 Xinsheng Wang, Tingting Qiao, Jihua Zhu, Alan Hanjalic, Odette Scharenborg

An estimated half of the world's languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies.

Image Generation

Robust Motion Averaging under Maximum Correntropy Criterion

no code implementations21 Apr 2020 Jihua Zhu, Jie Hu, Huimin Lu, Badong Chen, Zhongyu Li

Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem.

Generalized Label Enhancement with Sample Correlations

no code implementations7 Apr 2020 Qinghai Zheng, Jihua Zhu, Haoyu Tang, Xinyuan Liu, Zhongyu Li, Huimin Lu

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.

BIG-bench Machine Learning

Registration of multi-view point sets under the perspective of expectation-maximization

1 code implementation18 Feb 2020 Jihua Zhu, Jing Zhang, Huimin Lu, Zhongyu Li

Registration of multi-view point sets is a prerequisite for 3D model reconstruction.

Domain segmentation and adjustment for generalized zero-shot learning

no code implementations1 Feb 2020 Xinsheng Wang, Shanmin Pang, Jihua Zhu

In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes.

Generalized Zero-Shot Learning

Visual Space Optimization for Zero-shot Learning

no code implementations30 Jun 2019 Xinsheng Wang, Shanmin Pang, Jihua Zhu, Zhongyu Li, Zhiqiang Tian, Yaochen Li

The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive.

Zero-Shot Learning

Feature Concatenation Multi-view Subspace Clustering

1 code implementation30 Jan 2019 Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, Yaochen Li

To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.

Clustering Multi-view Subspace Clustering

Spatio-Temporal Road Scene Reconstruction using Superpixel Markov Random Field

no code implementations24 Nov 2018 Yaochen Li, Yuehu Liu, Jihua Zhu, Shiqi Ma, Zhenning Niu, Rui Guo

The data fidelity term in the MRF's energy function is jointly computed according to the superpixel features of color, texture and location.


Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval

1 code implementation22 May 2018 Shanmin Pang, Jin Ma, Jianru Xue, Jihua Zhu, Vicente Ordonez

We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of \emph{bursty} features.

Image Retrieval Re-Ranking +1

Adaptive Co-weighting Deep Convolutional Features For Object Retrieval

no code implementations20 Mar 2018 Jiaxing Wang, Jihua Zhu, Shanmin Pang, Zhongyu Li, Yaochen Li, Xueming Qian

Aggregating deep convolutional features into a global image vector has attracted sustained attention in image retrieval.

Image Retrieval Retrieval

K-means clustering for efficient and robust registration of multi-view point sets

no code implementations14 Oct 2017 Zutao Jiang, Jihua Zhu, Georgios D. Evangelidis, Changqing Zhang, Shanmin Pang, Yaochen Li

Subsequently, the shape comprised by all cluster centroids is used to sequentially estimate the rigid transformation for each point set.


Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions

no code implementations25 Sep 2017 Congcong Jin, Jihua Zhu, Yaochen Li, Shanmin Pang, Lei Chen, Jun Wang

Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability.

Simultaneous merging multiple grid maps using the robust motion averaging

no code implementations14 Jun 2017 Zutao Jiang, Jihua Zhu, Yaochen Li, Zhongyu Li, Huimin Lu

The main idea of this approach is to recover all global motions for map merging from a set of relative motions.

An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments

no code implementations1 Jun 2017 Congcong Jin, Jihua Zhu, Yaochen Li, Shaoyi Du, Zhongyu Li, Huimin Lu

For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments.

Effective scaling registration approach by imposing the emphasis on the scale factor

no code implementations28 Apr 2017 Minmin Xu, Siyu Xu, Jihua Zhu, Yaochen Li, Jun Wang, Huimin Lu

This paper proposes an effective approach for the scaling registration of $m$-D point sets.

Weighted Motion Averaging for the Registration of Multi-View Range Scans

no code implementations21 Feb 2017 Rui Guo, Jihua Zhu, Yaochen Li, Dapeng Chen, Zhongyu Li, Yongqin Zhang

With the overlapping percentage available, it views the overlapping percentage as the corresponding weight of each scan pair and proposes the weight motion averaging algorithm, which can pay more attention to reliable and accurate relative motions.

3D Reconstruction

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