Search Results for author: Bo Du

Found 152 papers, 94 papers with code

Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching

no code implementations29 Feb 2024 Boxuan Zhang, Zengmao Wang, Bo Du

The lack of object-level annotations poses a significant challenge for object detection in remote sensing images (RSIs).

Active Learning Object +3

ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding

no code implementations19 Feb 2024 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical.

Revisiting Knowledge Distillation for Autoregressive Language Models

no code implementations19 Feb 2024 Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, DaCheng Tao

Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model.

Knowledge Distillation

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

1 code implementation31 Jan 2024 Maoyuan Ye, Jing Zhang, Juhua Liu, Chenyu Liu, BaoCai Yin, Cong Liu, Bo Du, DaCheng Tao

In terms of the AMG mode, Hi-SAM segments text stroke foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing.

Segmentation Text Segmentation

Visual Imitation Learning with Calibrated Contrastive Representation

no code implementations21 Jan 2024 Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu

Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions.

Contrastive Learning Imitation Learning

MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images

no code implementations19 Jan 2024 Rui Xu, Yunke Wang, Bo Du

To address these two issues, we propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for unsupervised anomaly detection in brain images.

Unsupervised Anomaly Detection

Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models

1 code implementation17 Jan 2024 HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang, Deren Li

Recently, the flourishing large language models(LLM), especially ChatGPT, have shown exceptional performance in language understanding, reasoning, and interaction, attracting users and researchers from multiple fields and domains.

Transformer for Object Re-Identification: A Survey

no code implementations13 Jan 2024 Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David Crandall, Bo Du

Object Re-Identification (Re-ID) aims to identify and retrieve specific objects from varying viewpoints.

Object

OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models

1 code implementation12 Jan 2024 Shuai Wang, Liang Ding, Li Shen, Yong Luo, Bo Du, DaCheng Tao

Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP) in favor of functional programming (FP), e. g., HumanEval and MBPP.

Code Generation

XAI for In-hospital Mortality Prediction via Multimodal ICU Data

1 code implementation29 Dec 2023 Xingqiao Li, Jindong Gu, Zhiyong Wang, Yancheng Yuan, Bo Du, Fengxiang He

To address this issue, this paper proposes an eXplainable Multimodal Mortality Predictor (X-MMP) approaching an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data.

Decision Making Mortality Prediction

Joint Learning Neuronal Skeleton and Brain Circuit Topology with Permutation Invariant Encoders for Neuron Classification

1 code implementation22 Dec 2023 Minghui Liao, Guojia Wan, Bo Du

Skeleton Encoder integrates the local information of neurons in a bottom-up manner, with a one-dimensional convolution in neural skeleton's point data; Connectome Encoder uses a graph neural network to capture the topological information of neural circuit; finally, Readout Layer fuses the above two information and outputs classification results.

Sparse is Enough in Fine-tuning Pre-trained Large Language Model

1 code implementation19 Dec 2023 Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue.

Language Modelling Large Language Model

Noised Autoencoders for Point Annotation Restoration in Object Counting

no code implementations12 Dec 2023 Yuda Zou, Xin Xiao, Peilin Zhou, Zhichao Sun, Bo Du, Yongchao Xu

Object counting is a field of growing importance in domains such as security surveillance, urban planning, and biology.

General Knowledge Object +1

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion

1 code implementation11 Dec 2023 Anke Tang, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, DaCheng Tao

At the upper level, we focus on learning a shared Concrete mask to identify the subspace, while at the inner level, model merging is performed to maximize the performance of the merged model.

Meta-Learning

Exploring Sparsity in Graph Transformers

no code implementations9 Dec 2023 Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, Wenbin Hu, Bo Du

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.

Adapting Vision Transformer for Efficient Change Detection

no code implementations8 Dec 2023 Yang Zhao, Yuxiang Zhang, Yanni Dong, Bo Du

Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy.

Change Detection

UniGS: Unified Representation for Image Generation and Segmentation

1 code implementation4 Dec 2023 Lu Qi, Lehan Yang, Weidong Guo, Yu Xu, Bo Du, Varun Jampani, Ming-Hsuan Yang

On the other hand, the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers.

Image Generation Segmentation

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes

no code implementations21 Nov 2023 Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Bo Du, Wenbin Hu

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.

Graph Representation Learning

Learning transformer-based heterogeneously salient graph representation for multimodal fusion classification of hyperspectral image and LiDAR data

no code implementations17 Nov 2023 Jiaqi Yang, Bo Du, Liangpei Zhang

Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation.

Image Classification Remote Sensing Image Classification

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

1 code implementation12 Nov 2023 Wenke Huang, Mang Ye, Zekun Shi, Guancheng Wan, He Li, Bo Du, Qiang Yang

In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.

Fairness Federated Learning +1

Rotation Invariant Transformer for Recognizing Object in UAVs

2 code implementations ACM Multimedia 2022 Shuoyi Chen, Mang Ye, Bo Du

Existing methods are usually designed for city cameras, incapable of handing the rotation issue in UAV scenarios.

Object Person Re-Identification +1

Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models

no code implementations20 Oct 2023 Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem.

Language Modelling Quantization

Learn From Model Beyond Fine-Tuning: A Survey

1 code implementation12 Oct 2023 Hongling Zheng, Li Shen, Anke Tang, Yong Luo, Han Hu, Bo Du, DaCheng Tao

LFM focuses on the research, modification, and design of FM based on the model interface, so as to better understand the model structure and weights (in a black box environment), and to generalize the model to downstream tasks.

Meta-Learning Model Editing

Imitation Learning from Purified Demonstration

no code implementations11 Oct 2023 Yunke Wang, Minjing Dong, Bo Du, Chang Xu

To tackle these problems, we propose to purify the potential perturbations in imperfect demonstrations and subsequently conduct imitation learning from purified demonstrations.

Imitation Learning

Parameter Efficient Multi-task Model Fusion with Partial Linearization

no code implementations7 Oct 2023 Anke Tang, Li Shen, Yong Luo, Yibing Zhan, Han Hu, Bo Du, Yixin Chen, DaCheng Tao

We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone.

Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange

1 code implementation1 Oct 2023 Hongruixuan Chen, Jian Song, Chen Wu, Bo Du, Naoto Yokoya

Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images.

Change Detection Image Enhancement +1

Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning

2 code implementations28 Sep 2023 Wenke Huang, Mang Ye, Zekun Shi, Bo Du

Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data.

Domain Generalization Federated Learning +1

BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks

1 code implementation31 Aug 2023 Qiang Huang, Jiawei Jiang, Xi Susie Rao, Ce Zhang, Zhichao Han, Zitao Zhang, Xin Wang, Yongjun He, Quanqing Xu, Yang Zhao, Chuang Hu, Shuo Shang, Bo Du

To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.

Link Prediction Node Classification

SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images

no code implementations28 Aug 2023 HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang

These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages.

Change Detection

Enhancing Visually-Rich Document Understanding via Layout Structure Modeling

1 code implementation15 Aug 2023 Qiwei Li, Zuchao Li, Xiantao Cai, Bo Du, Hai Zhao

In this paper, we propose GraphLayoutLM, a novel document understanding model that leverages the modeling of layout structure graph to inject document layout knowledge into the model.

document understanding

Rethinking the Localization in Weakly Supervised Object Localization

no code implementations11 Aug 2023 Rui Xu, Yong Luo, Han Hu, Bo Du, Jialie Shen, Yonggang Wen

Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision.

Object Weakly-Supervised Object Localization

Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

1 code implementation28 Jul 2023 Zhihao LI, Jiancheng Yang, Yongchao Xu, Li Zhang, Wenhui Dong, Bo Du

Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods.

Image Segmentation Management +4

IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer

1 code implementation27 Jul 2023 Xiaochen Ma, Bo Du, Zhuohang Jiang, Ahmed Y. Al Hammadi, Jizhe Zhou

To bridge this gap, based on the fact that artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision that could converge with a small amount of data.

Benchmarking Image Manipulation +1

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

1 code implementation26 Jul 2023 Wenjie Xuan, Shanshan Zhao, Yu Yao, Juhua Liu, Tongliang Liu, Yixin Chen, Bo Du, DaCheng Tao

Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels.

Edge Detection

Expediting Building Footprint Segmentation from High-resolution Remote Sensing Images via progressive lenient supervision

1 code implementation23 Jul 2023 HaoNan Guo, Bo Du, Chen Wu, Xin Su, Liangpei Zhang

The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness.

Segmentation

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

1 code implementation23 Jul 2023 HaoNan Guo, Bo Du, Chen Wu, Chengxi Han, Liangpei Zhang

To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD.

Change Detection Metric Learning

Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction

no code implementations23 Jul 2023 HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang

Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks.

Heterogeneous Federated Learning: State-of-the-art and Research Challenges

2 code implementations20 Jul 2023 Mang Ye, Xiuwen Fang, Bo Du, Pong C. Yuen, DaCheng Tao

Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential.

Federated Learning

Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers

1 code implementation2 Jul 2023 Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

SGD and Adam are two classical and effective optimizers on which researchers have proposed many variants, such as SGDM and RAdam.

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

1 code implementation22 Jun 2023 Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu

A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology.

Graph Classification Representation Learning

FHA-Kitchens: A Novel Dataset for Fine-Grained Hand Action Recognition in Kitchen Scenes

1 code implementation19 Jun 2023 Ting Zhe, YongQian Li, Jing Zhang, Yong Luo, Han Hu, Bo Du, Yonggang Wen, DaCheng Tao

We represent the action information in each hand interaction region as a triplet, resulting in a total of 878 action triplets.

Action Recognition Domain Generalization +3

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction

1 code implementation19 Jun 2023 Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework.

UIE

Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution

1 code implementation1 Jun 2023 Wuxuan Shi, Mang Ye, Bo Du

(2) For the cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove parts of RGB information harmful to the recovery of HR depth maps.

Super-Resolution

DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting

2 code implementations31 May 2023 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, DaCheng Tao

On the other hand, based on the extensibility of DeepSolo, we launch DeepSolo++ for multilingual text spotting, making a further step to let Transformer decoder with explicit points solo for multilingual text detection, recognition, and script identification all at once.

Scene Text Detection Text Detection +1

AIMS: All-Inclusive Multi-Level Segmentation

1 code implementation28 May 2023 Lu Qi, Jason Kuen, Weidong Guo, Jiuxiang Gu, Zhe Lin, Bo Du, Yu Xu, Ming-Hsuan Yang

Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved.

Image Segmentation Segmentation +1

Self-Evolution Learning for Discriminative Language Model Pretraining

1 code implementation24 May 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

Masked language modeling, widely used in discriminative language model (e. g., BERT) pretraining, commonly adopts a random masking strategy.

Language Modelling Masked Language Modeling +1

Revisiting Token Dropping Strategy in Efficient BERT Pretraining

1 code implementation24 May 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Xuebo Liu, Min Zhang, Bo Du, DaCheng Tao

Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers.

Improving Heterogeneous Model Reuse by Density Estimation

1 code implementation23 May 2023 Anke Tang, Yong Luo, Han Hu, Fengxiang He, Kehua Su, Bo Du, Yixin Chen, DaCheng Tao

This paper studies multiparty learning, aiming to learn a model using the private data of different participants.

Density Estimation Selection bias

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

2 code implementations NeurIPS 2023 Di Wang, Jing Zhang, Bo Du, Minqiang Xu, Lin Liu, DaCheng Tao, Liangpei Zhang

In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS.

Instance Segmentation Object +4

Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey

no code implementations3 May 2023 Tao Chen, Liang Lv, Di Wang, Jing Zhang, Yue Yang, Zeyang Zhao, Chen Wang, Xiaowei Guo, Hao Chen, Qingye Wang, Yufei Xu, Qiming Zhang, Bo Du, Liangpei Zhang, DaCheng Tao

With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.

Scalable Mask Annotation for Video Text Spotting

1 code implementation2 May 2023 Haibin He, Jing Zhang, Mengyang Xu, Juhua Liu, Bo Du, DaCheng Tao

Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames.

Text Spotting

HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search

1 code implementation23 Apr 2023 Di Wang, Bo Du, Liangpei Zhang, DaCheng Tao

Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks.

Neural Architecture Search

DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification

2 code implementations19 Apr 2023 Di Wang, Jing Zhang, Bo Du, Liangpei Zhang, DaCheng Tao

Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions.

Hyperspectral Image Classification Image Generation

Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data

no code implementations3 Apr 2023 Rui Xu, Yong Luo, Bo Du

This motivates us to propose a Source-free Unsupervised cross-domain method for Pulmonary nodule detection (SUP).

Contrastive Learning Unsupervised Domain Adaptation

EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

no code implementations24 Mar 2023 Meiqi Hu, Chen Wu, Bo Du

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration.

Change Detection Clustering

Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

2 code implementations CVPR 2023 Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu

Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

Centroid-centered Modeling for Efficient Vision Transformer Pre-training

no code implementations8 Mar 2023 Xin Yan, Zuchao Li, Lefei Zhang, Bo Du, DaCheng Tao

Our proposed approach, \textbf{CCViT}, leverages k-means clustering to obtain centroids for image modeling without supervised training of tokenizer model.

Semantic Segmentation

SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

1 code implementation7 Mar 2023 Rui Xu, Zhi Liu, Yong Luo, Han Hu, Li Shen, Bo Du, Kaiming Kuang, Jiancheng Yang

To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.

Computed Tomography (CT)

Token Contrast for Weakly-Supervised Semantic Segmentation

1 code implementation CVPR 2023 Lixiang Ru, Heliang Zheng, Yibing Zhan, Bo Du

Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection

no code implementations21 Feb 2023 Chengxi Han, Chen Wu, Bo Du

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem.

Change Detection

Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

1 code implementation19 Feb 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries.

Question Answering Sentiment Analysis

Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE

no code implementations18 Feb 2023 Qihuang Zhong, Liang Ding, Keqin Peng, Juhua Liu, Bo Du, Li Shen, Yibing Zhan, DaCheng Tao

This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.

Contrastive Learning Denoising +12

Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning

no code implementations16 Feb 2023 Zhihao Qian, Yutian Lin, Bo Du

In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning.

Image Generation Person Re-Identification +2

Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning

1 code implementation13 Feb 2023 Yunke Wang, Bo Du, Chang Xu

The trajectories of an initial agent policy could be closer to those non-optimal expert demonstrations, but within the framework of adversarial imitation learning, agent policy will be optimized to cheat the discriminator and produce trajectories that are similar to those optimal expert demonstrations.

Imitation Learning

Generating Dynamic Kernels via Transformers for Lane Detection

1 code implementation ICCV 2023 Ziye Chen, Yu Liu, Mingming Gong, Bo Du, Guoqi Qian, Kate Smith-Miles

While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies.

Lane Detection

Diff-Font: Diffusion Model for Robust One-Shot Font Generation

1 code implementation12 Dec 2022 Haibin He, Xinyuan Chen, Chaoyue Wang, Juhua Liu, Bo Du, DaCheng Tao, Yu Qiao

Specifically, a large stroke-wise dataset is constructed, and a stroke-wise diffusion model is proposed to preserve the structure and the completion of each generated character.

Font Generation

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

2 code implementations CVPR 2023 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, DaCheng Tao

In this paper, we present DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously.

 Ranked #1 on Text Spotting on Total-Text (using extra training data)

Scene Text Detection Text Detection +2

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

1 code implementation2 Nov 2022 Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Akbar Telikani, Jianqing Wu, Shubin Xie

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).

Q-Learning

Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models

1 code implementation11 Oct 2022 Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, DaCheng Tao

Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization.

Few-Shot Model Agnostic Federated Learning

2 code implementations Proceedings of the 30th ACM International Conference on Multimedia 2022 Wenke Huang, Mang Ye, Bo Du, Xiang Gao

To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared public datasets; 2) latent embedding adaptation, it addresses the domain gap with an adversarial learning scheme to discriminate the public and private domains.

Federated Learning

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

1 code implementation3 Oct 2022 Hongruixuan Chen, Naoto Yokoya, Chen Wu, Bo Du

Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels.

Change Detection Graph Representation Learning

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

no code implementations7 Sep 2022 Mengya Han, Yibing Zhan, Yong Luo, Bo Du, Han Hu, Yonggang Wen, DaCheng Tao

To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition.

Meta-Learning Representation Learning

PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation

no code implementations22 Aug 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

In response to these problems, we propose a new metric to accurately predict the prompt transferability (regarding (i)), and a novel PoT approach (namely PANDA) that leverages the knowledge distillation technique to transfer the "knowledge" from the source prompt to the target prompt in a subtle manner and alleviate the catastrophic forgetting effectively (regarding (ii)).

Knowledge Distillation Transfer Learning

Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

2 code implementations8 Aug 2022 Di Wang, Qiming Zhang, Yufei Xu, Jing Zhang, Bo Du, DaCheng Tao, Liangpei Zhang

Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability.

Aerial Scene Classification Few-Shot Learning +2

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

1 code implementation3 Aug 2022 Rui Xu, Yong Luo, Bo Du, Kaiming Kuang, Jiancheng Yang

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection.

Computed Tomography (CT)

Leveraging GAN Priors for Few-Shot Part Segmentation

1 code implementation27 Jul 2022 Mengya Han, Heliang Zheng, Chaoyue Wang, Yong Luo, Han Hu, Bo Du

Overall, this work is an attempt to explore the internal relevance between generation tasks and perception tasks by prompt designing.

Image Generation Segmentation

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks

no code implementations18 Jul 2022 Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, Danilo Mandic

However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.

Node Classification

Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning

no code implementations14 Jul 2022 Xingping Dong, Shengcai Liao, Bo Du, Ling Shao

Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit.

Few-Shot Learning

DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer

3 code implementations10 Jul 2022 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Bo Du, DaCheng Tao

However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation.

Inductive Bias Scene Text Detection +1

What Makes for Automatic Reconstruction of Pulmonary Segments

1 code implementation7 Jul 2022 Kaiming Kuang, Li Zhang, Jingyu Li, Hongwei Li, Jiajun Chen, Bo Du, Jiancheng Yang

The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.

3D Reconstruction

CLNode: Curriculum Learning for Node Classification

1 code implementation15 Jun 2022 Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu

Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.

Classification Node Classification

E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation

1 code implementation30 May 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation.

Denoising Language Modelling +2

Robust Weight Perturbation for Adversarial Training

1 code implementation30 May 2022 Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu

Based on these observations, we propose a robust perturbation strategy to constrain the extent of weight perturbation.

Classification

Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection

no code implementations23 May 2022 Meiqi Hu, Chen Wu, Bo Du

Hyperspectral anomalous change detection has been a challenging task for its emphasis on the dynamics of small and rare objects against the prevalent changes.

Change Detection Contrastive Learning

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

1 code implementation15 Apr 2022 Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, DaCheng Tao

Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.

Graph Classification Graph Generation

An Empirical Study of Remote Sensing Pretraining

2 code implementations6 Apr 2022 Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, DaCheng Tao

To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.

Aerial Scene Classification Building change detection for remote sensing images +5

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

1 code implementation1 Apr 2022 Jia Liu, Wenjie Xuan, Yuhang Gan, Juhua Liu, Bo Du

In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.

Change Detection Change detection for remote sensing images +1

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

1 code implementation CVPR 2022 Lixiang Ru, Yibing Zhan, Baosheng Yu, Bo Du

Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Multi-Tailed Vision Transformer for Efficient Inference

no code implementations3 Mar 2022 Yunke Wang, Bo Du, Wenyuan Wang, Chang Xu

To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length.

Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling

1 code implementation10 Feb 2022 Lixiang Ru, Bo Du, Yibing Zhan, Chen Wu

In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.

Classification Weakly supervised Semantic Segmentation +1

Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation

1 code implementation18 Jan 2022 Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du

To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.

Graph Generation Unbiased Scene Graph Generation

Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

1 code implementation13 Jan 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Hua Jin, DaCheng Tao

To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

Learn From Others and Be Yourself in Heterogeneous Federated Learning

1 code implementation CVPR 2022 Wenke Huang, Mang Ye, Bo Du

Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data.

Continual Learning Federated Learning +2

Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization

1 code implementation CVPR 2022 Ziyi Liu, Zengmao Wang, Bo Du

In this paper, we propose a deep protein subcellular localization method with multi-marginal contrastive learning to perceive the same PSLs in different tissue images and different PSLs within the same tissue image.

Contrastive Learning

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

1 code implementation AAAI 2022 2021 Yue He, Chen Chen, Jing Zhang, Juhua Liu, Fengxiang He, Chaoyue Wang, Bo Du

Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.

Ranked #9 on Scene Text Recognition on ICDAR2015 (using extra training data)

Language Modelling Scene Text Recognition

Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

1 code implementation19 Dec 2021 Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.

Reinforcement Learning (RL)

Self-Ensembling GAN for Cross-Domain Semantic Segmentation

1 code implementation15 Dec 2021 Yonghao Xu, Fengxiang He, Bo Du, DaCheng Tao, Liangpei Zhang

In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN.

Generative Adversarial Network Segmentation +1

Binary Change Guided Hyperspectral Multiclass Change Detection

no code implementations8 Dec 2021 Meiqi Hu, Chen Wu, Bo Du, Liangpei Zhang

In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches.

Change Detection

Efficient Pressure: Improving efficiency for signalized intersections

1 code implementation4 Dec 2021 Qiang Wu, Liang Zhang, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.

Reinforcement Learning (RL)

MixSiam: A Mixture-based Approach to Self-supervised Representation Learning

no code implementations4 Nov 2021 Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du

In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.

Contrastive Learning Representation Learning

Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

1 code implementation26 Oct 2021 Juhua Liu, Qihuang Zhong, Liang Ding, Hua Jin, Bo Du, DaCheng Tao

In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network

no code implementations15 Oct 2021 Ziyi Liu, Minghui Liao, Fulin Luo, Bo Du

This method constructs the graph by the similarity relationship between cells and adopts GCN to analyze the neighbor embedding information of samples, which makes the similar cell closer to each other on the 2D scatter plot.

Dimensionality Reduction Graph Embedding

Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer

no code implementations18 Sep 2021 Hongruixuan Chen, Chen Wu, Yonghao Xu, Bo Du

To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.

Ranked #33 on Synthetic-to-Real Translation on GTAV-to-Cityscapes Labels (using extra training data)

Self-Supervised Learning Semantic Segmentation +2

Self-supervised Contrastive Learning for EEG-based Sleep Staging

1 code implementation16 Sep 2021 Xue Jiang, Jianhui Zhao, Bo Du, Zhiyong Yuan

In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.

Contrastive Learning EEG +3

Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images

1 code implementation18 Aug 2021 Hongruixuan Chen, Chen Wu, Bo Du

With the goal of designing a quite deep architecture to obtain more precise CD results while simultaneously decreasing parameter numbers to improve efficiency, in this work, we present a very deep and efficient CD network, entitled EffCDNet.

Change Detection

DeepFake MNIST+: A DeepFake Facial Animation Dataset

1 code implementation18 Aug 2021 Jiajun Huang, Xueyu Wang, Bo Du, Pei Du, Chang Xu

It includes 10, 000 facial animation videos in ten different actions, which can spoof the recent liveness detectors.

DeepFake Detection Face Swapping +1

Unsupervised Person Re-identification with Stochastic Training Strategy

2 code implementations16 Aug 2021 Tianyang Liu, Yutian Lin, Bo Du

State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.

Clustering Contrastive Learning +1

I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection

1 code implementation3 Aug 2021 Bo Du, Jian Ye, Jing Zhang, Juhua Liu, DaCheng Tao

Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.

Scene Text Detection Text Detection

Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification

2 code implementations26 Jun 2021 Di Wang, Bo Du, Liangpei Zhang

To tackle these problems, in this paper, different from previous approaches, we perform the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes.

Classification Hyperspectral Image Classification

Robust Self-Ensembling Network for Hyperspectral Image Classification

1 code implementation8 Apr 2021 Yonghao Xu, Bo Du, Liangpei Zhang

Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance.

Classification General Classification +1

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

no code implementations2 Mar 2021 Chen Wu, Sihan Zhu, Jiaqi Yang, Meiqi Hu, Bo Du, Liangpei Zhang, Lefei Zhang, Chengxi Han, Meng Lan

Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.

LocalDrop: A Hybrid Regularization for Deep Neural Networks

no code implementations1 Mar 2021 Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.

A high-sensitivity fiber-coupled diamond magnetometer with surface coating

no code implementations24 Feb 2021 Shao-Chun Zhang, Hao-Bin Lin, Yang Dong, Bo Du, Xue-Dong Gao, Cui Yu, Zhi-Hong Feng, Xiang-Dong Chen, Guang-Can Guo, Fang-Wen Sun

Nitrogen-vacancy quantum defects in diamond offer a promising platform for magnetometry because of their remarkable optical and spin properties.

Applied Physics Quantum Physics

Channel Augmented Joint Learning for Visible-Infrared Recognition

1 code implementation ICCV 2021 Mang Ye, Weijian Ruan, Bo Du, Mike Zheng Shou

This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem.

Data Augmentation Metric Learning

Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search

no code implementations ICCV 2021 Ziye Chen, Yibing Zhan, Baosheng Yu, Mingming Gong, Bo Du

Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.

Neural Architecture Search

Leveraged Matrix Completion with Noise

no code implementations11 Nov 2020 Xinjian Huang, Weiwei Liu, Bo Du, DaCheng Tao

In this paper, we employ the leverage scores to characterize the importance of each element and significantly relax assumptions to: (1) not any other structure assumptions are imposed on the underlying low-rank matrix; (2) elements being observed are appropriately dependent on their importance via the leverage score.

Matrix Completion

Hyperspectral Anomaly Change Detection Based on Auto-encoder

1 code implementation27 Oct 2020 Meiqi Hu, Chen Wu, Liangpei Zhang, Bo Du

In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions.

Change Detection

Semantic Change Detection with Asymmetric Siamese Networks

1 code implementation12 Oct 2020 Kunping Yang, Gui-Song Xia, Zicheng Liu, Bo Du, Wen Yang, Marcello Pelillo, Liangpei Zhang

Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.

Change Detection Management

Recurrent Feature Reasoning for Image Inpainting

1 code implementation CVPR 2020 Jingyuan Li, Ning Wang, Lefei Zhang, Bo Du, DaCheng Tao

To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.

Image Inpainting SSIM

Designing and Training of A Dual CNN for Image Denoising

1 code implementation8 Jul 2020 Chunwei Tian, Yong Xu, WangMeng Zuo, Bo Du, Chia-Wen Lin, David Zhang

The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.

Image Denoising

An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images

no code implementations26 Jun 2020 Chen Wu, Yinong Guo, HaoNan Guo, Jingwen Yuan, Lixiang Ru, Hongruixuan Chen, Bo Du, Liangpei Zhang

The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

Anomaly Detection Time Series +1

DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection

no code implementations16 Jun 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains.

Change Detection Domain Adaptation

Self-supervised Training of Graph Convolutional Networks

1 code implementation3 Jun 2020 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.

Data Augmentation Self-Supervised Learning

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

1 code implementation3 Jun 2020 Lixiang Ru, Bo Du, Chen Wu

In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.

Change Detection General Classification +2

TextFuseNet: Scene Text Detection with Richer Fused Features

6 code implementations17 May 2020 Jian Ye, Zhe Chen, Juhua Liu, Bo Du

More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.

Scene Text Detection Text Detection

Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images

no code implementations13 Apr 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangepei Zhang

In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection.

Change Detection Domain Adaptation

OASIS: One-pass aligned Atlas Set for Image Segmentation

no code implementations5 Dec 2019 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.

Image Registration Image Segmentation +3

Multi-hop Convolutions on Weighted Graphs

1 code implementation12 Nov 2019 Qikui Zhu, Bo Du, Pingkun Yan

To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.

Unified Multi-scale Feature Abstraction for Medical Image Segmentation

no code implementations24 Oct 2019 Xi Fang, Bo Du, Sheng Xu, Bradford J. Wood, Pingkun Yan

Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.

Image Segmentation Medical Image Segmentation +2

Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

3 code implementations27 Jun 2019 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively.

Change Detection

Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

1 code implementation14 May 2019 Sheng Wan, Chen Gong, Ping Zhong, Bo Du, Lefei Zhang, Jian Yang

To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.

Classification General Classification +1

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

no code implementations8 Apr 2019 Lefei Zhang, Qian Zhang, Bo Du, Xin Huang, Yuan Yan Tang, DaCheng Tao

In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.

Dimensionality Reduction feature selection +2

Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

1 code implementation21 Feb 2019 Qikui Zhu, Bo Du, Pingkun Yan

To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.

Image Segmentation Medical Image Segmentation +2

Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

1 code implementation3 Dec 2018 Bo Du, Lixiang Ru, Chen Wu, Liangpei Zhang

In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection.

Change Detection

Defect Detection from UAV Images based on Region-Based CNNs

no code implementations23 Nov 2018 Meng Lan, YiPeng Zhang, Lefei Zhang, Bo Du

In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.

Defect Detection object-detection +1

TLR: Transfer Latent Representation for Unsupervised Domain Adaptation

no code implementations19 Aug 2018 Pan Xiao, Bo Du, Jia Wu, Lefei Zhang, Ruimin Hu, Xuelong. Li

Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.

Unsupervised Domain Adaptation

Unsupervised Domain Adaptive Re-Identification: Theory and Practice

3 code implementations30 Jul 2018 Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, Xinggang Wang

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.

General Classification Unsupervised Domain Adaptation

Hyperspectral image classification via a random patches network

1 code implementation ISPRS Journal of Photogrammetry and Remote Sensing 2018 Yonghao Xu, Bo Du, Fan Zhang, Liangpei Zhang

Due to the remarkable achievements obtained by deep learning methods in the fields of computer vision, an increasing number of researches have been made to apply these powerful tools into hyperspectral image (HSI) classification.

Classification Few-Shot Image Classification +1

Deeply-Supervised CNN for Prostate Segmentation

no code implementations22 Mar 2017 Qikui Zhu, Bo Du, Baris Turkbey, Peter L . Choyke, Pingkun Yan

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.

Segmentation

A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data

no code implementations26 Feb 2017 Fan Zhang, Bo Du, Liangpei Zhang

For the second target, a novel CNN-based universal framework is proposed to process the VHR satellite images and generate the land-use, urban density, and population distribution maps.

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