Search Results for author: Jiahua Dong

Found 32 papers, 13 papers with code

Federated Learning with New Knowledge: Fundamentals, Advances, and Futures

1 code implementation3 Feb 2024 Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.

Federated Learning Privacy Preserving

ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields

1 code implementation NeurIPS 2023 Jiahua Dong, Yu-Xiong Wang

In addition to the implicit neural radiance field (NeRF) modeling, our key insight is to exploit two sources of regularization that explicitly propagate the editing information across different views, thus ensuring multi-view consistency.

MM-LLMs: Recent Advances in MultiModal Large Language Models

no code implementations24 Jan 2024 Duzhen Zhang, Yahan Yu, Chenxing Li, Jiahua Dong, Dan Su, Chenhui Chu, Dong Yu

In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.

Decision Making

Federated Continual Novel Class Learning

no code implementations21 Dec 2023 Lixu Wang, Chenxi Liu, Junfeng Guo, Jiahua Dong, Xiao Wang, Heng Huang, Qi Zhu

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.

Federated Learning Novel Class Discovery +1

Continual Named Entity Recognition without Catastrophic Forgetting

1 code implementation23 Oct 2023 Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, Zhen Fang

This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type.

Continual Named Entity Recognition named-entity-recognition +1

Create Your World: Lifelong Text-to-Image Diffusion

no code implementations8 Sep 2023 Gan Sun, Wenqi Liang, Jiahua Dong, Jun Li, Zhengming Ding, Yang Cong

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc.

Attribute Image Generation

I3DOD: Towards Incremental 3D Object Detection via Prompting

no code implementations24 Aug 2023 Wenqi Liang, Gan Sun, Chenxi Liu, Jiahua Dong, Kangru Wang

Meanwhile, the current class-incremental 3D object detection methods neglect the relationships between the object localization information and category semantic information and assume all the knowledge of old model is reliable.

3D Object Detection Autonomous Driving +3

Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition

1 code implementation17 Aug 2023 Duzhen Zhang, Hongliu Li, Wei Cong, Rongtao Xu, Jiahua Dong, Xiuyi Chen

However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).

Incremental Learning named-entity-recognition +3

Heterogeneous Forgetting Compensation for Class-Incremental Learning

1 code implementation ICCV 2023 Jiahua Dong, Wenqi Liang, Yang Cong, Gan Sun

To surmount the above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous forgetting of easy-to-forget and hard-to-forget old categories from both representation and gradient aspects.

Class Incremental Learning Incremental Learning +1

Self-paced Weight Consolidation for Continual Learning

no code implementations20 Jul 2023 Wei Cong, Yang Cong, Gan Sun, Yuyang Liu, Jiahua Dong

Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings.

Continual Learning

Gradient-Semantic Compensation for Incremental Semantic Segmentation

no code implementations20 Jul 2023 Wei Cong, Yang Cong, Jiahua Dong, Gan Sun, Henghui Ding

To tackle the above challenges, in this paper, we propose a Gradient-Semantic Compensation (GSC) model, which surmounts incremental semantic segmentation from both gradient and semantic perspectives.

Segmentation Semantic Segmentation

Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse

no code implementations30 Apr 2023 Anqi Wang, Jiahua Dong, Jiachuan Shen, Lik-Hang Lee, Pan Hui

We discuss 187 articles (80. 7% of articles published between 2018-2022) to review the field of generated possibilities of architecture in virtual environments, limited to the architecture form.

3D Shape Generation

Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble

no code implementations14 Apr 2023 Jiahua Dong, Guohua Cheng, Yue Zhang, Chengtao Peng, Yu Song, Ruofeng Tong, Lanfen Lin, Yen-Wei Chen

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.

Organ Segmentation Segmentation

Federated Incremental Semantic Segmentation

1 code implementation CVPR 2023 Jiahua Dong, Duzhen Zhang, Yang Cong, Wei Cong, Henghui Ding, Dengxin Dai

Moreover, new clients collecting novel classes may join in the global training of FSS, which further exacerbates catastrophic forgetting.

Federated Learning Relation +2

Xformer: Hybrid X-Shaped Transformer for Image Denoising

no code implementations11 Mar 2023 Jiale Zhang, Yulun Zhang, Jinjin Gu, Jiahua Dong, Linghe Kong, Xiaokang Yang

In this paper, we present a hybrid X-shaped vision Transformer, named Xformer, which performs notably on image denoising tasks.

Image Denoising

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

no code implementations20 Feb 2023 Jiahua Dong, Yang Cong, Gan Sun, Lixu Wang, Lingjuan Lyu, Jun Li, Ender Konukoglu

Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.

3D Object Recognition Fairness

No One Left Behind: Real-World Federated Class-Incremental Learning

2 code implementations2 Feb 2023 Jiahua Dong, Hongliu Li, Yang Cong, Gan Sun, Yulun Zhang, Luc van Gool

These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories.

Class Incremental Learning Federated Learning +1

Is Out-of-Distribution Detection Learnable?

no code implementations26 Oct 2022 Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu

Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios.

Learning Theory Out-of-Distribution Detection +2

BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM

2 code implementations15 Aug 2022 Yunge Cui, Xieyuanli Chen, Yinlong Zhang, Jiahua Dong, Qingxiao Wu, Feng Zhu

To address this limitation, we present a novel Bag of Words for real-time loop closing in 3D LiDAR SLAM, called BoW3D.

Simultaneous Localization and Mapping

LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud

2 code implementations13 Jun 2022 Yunge Cui, Yinlong Zhang, Jiahua Dong, Haibo Sun, Xieyuanli Chen, Feng Zhu

Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration.

3D Object Detection object-detection

Federated Class-Incremental Learning

1 code implementation CVPR 2022 Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu

It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes.

Class Incremental Learning Federated Learning +1

Confident Anchor-Induced Multi-Source Free Domain Adaptation

1 code implementation NeurIPS 2021 Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu

To address these challenges, we develop a novel Confident-Anchor-induced multi-source-free Domain Adaptation (CAiDA) model, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models.

Pseudo Label Source-Free Domain Adaptation +1

Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence

no code implementations ICCV 2021 Ronghan Chen, Yang Cong, Jiahua Dong

Shape correspondence from 3D deformation learning has attracted appealing academy interests recently.

Generative Partial Visual-Tactile Fused Object Clustering

no code implementations28 Dec 2020 Tao Zhang, Yang Cong, Gan Sun, Jiahua Dong, Yuyang Liu, Zhengming Ding

More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces.

Clustering Generative Adversarial Network +2

I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting

no code implementations16 Dec 2020 Jiahua Dong, Yang Cong, Gan Sun, Bingtao Ma, Lichen Wang

Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i. e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data.

3D Object Classification Fairness +2

Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation

no code implementations8 Dec 2020 Jiahua Dong, Yang Cong, Gan Sun, Yunsheng Yang, Xiaowei Xu, Zhengming Ding

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost.

Domain Adaptation Pseudo Label +1

CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation

no code implementations ECCV 2020 Jiahua Dong, Yang Cong, Gan Sun, Yuyang Liu, Xiaowei Xu

Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation.

Semantic Segmentation Unsupervised Domain Adaptation

Evolving Metric Learning for Incremental and Decremental Features

no code implementations27 Jun 2020 Jiahua Dong, Yang Cong, Gan Sun, Tao Zhang, Xu Tang, Xiaowei Xu

Online metric learning has been widely exploited for large-scale data classification due to the low computational cost.

Metric Learning

Data Poisoning Attacks on Federated Machine Learning

no code implementations19 Apr 2020 Gan Sun, Yang Cong, Jiahua Dong, Qiang Wang, Ji Liu

To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.

BIG-bench Machine Learning Data Poisoning +2

Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation

1 code implementation ICCV 2019 Jiahua Dong, Yang Cong, Gan Sun, Dongdong Hou

To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task.

Pseudo Label Segmentation +2

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