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arXiv 2024年

作者： Shan, Lianlei Wang, Weiqiang Lv, Ke Luo, Bin The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei230601 China

Semantic segmentation requires pixel-level annotation, which is time-consuming. Active Learning (AL) is a promising method for reducing data annotation costs. Due to the gap between aerial and natural images, the previous AL methods are not ideal, mainly caused by unreasonable labeling units and the neglect of class imbalance. Previous labeling units are based on images or regions, which does not consider the characteristics of segmentation tasks and aerial images, i.e., the segmentation network often makes mistakes in the edge region, and the edge of aerial images is often interlaced and irregular. Therefore, an edge-guided labeling unit is proposed and supplemented as the new unit. On the other hand, the class imbalance is severe, manifested in two aspects: the aerial image is seriously imbalanced, and the AL strategy does not fully consider the class balance. Both seriously affect the performance of AL in aerial images. We comprehensively ensure class balance from all steps that may occur imbalance, including initial labeled data, subsequent labeled data, and pseudo-labels. Through the two improvements, our method achieves more than 11.2% gains compared to state-of-the-art methods on three benchmark datasets, Deepglobe, Potsdam, and Vaihingen, and more than 18.6% gains compared to the baseline. Sufficient ablation studies show that every module is indispensable. Furthermore, we establish a fair and strong benchmark for future research on AL for aerial image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Data-Free Network Debiasing for Long-Tailed Visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection 24

HD-KT: Advancing Robust Knowledge Tracing via Anomalous Lear...

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33rd ACM Web Conference, WWW 2024

作者： Ma, Haiping Yang, Yong Qin, Chuan Yu, Xiaoshan Yang, Shangshang Zhang, Xingyi Zhu, Hengshu Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Beijing China School of Artificial Intelligence Anhui University Anhui Hefei China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin Beijing China

ISBN: (纸本)9798400701719

Knowledge tracing (KT) is a crucial task in online learning, aimed at tracing and predicting each student's knowledge states throughout their learning process. Over the past decade, it has garnered widespread attention due to it provides the potential for more tailored and adaptive online learning experiences. Although most current KT methodologies emphasize optimizing network structures to enhance predictive accuracy for future student performance, they often neglect anomalous interactions in students' learning processes, which may arise from low data quality (i.e., inferior question quality) and abnormal student behaviors (i.e., guessing and mistakes). To this end, in this paper, we propose a novel framework, termed HD-KT, designed to enhance the robustness of existing KT methodologies with Hybrid learning interactions Denoising approach. Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. In the first detection module, we design a sequential autoencoder to identify anomalous learning interactions by detecting atypical student knowledge states. In the second module, we incorporate an attention mechanism by modeling a student's long-term profile to capture irregular interactions. Extensive experiments on four real-world benchmark datasets have decisively shown our HD-KT markedly boosts the robustness of numerous prevailing KT models, consequently increasing the accuracy of future student performance predictions. Additionally, our case studies highlight the versatility of HD-KT in addressing diverse downstream tasks, such as exercise quality analysis and learning behavior-based student clustering. © 2024 ACM.

关键词： Students

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High Capacity Reversible Data Hiding Scheme based on Interpolation and Tetris Matrix

High Capacity Reversible Data Hiding Scheme based on Interpo...

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第41届中国控制会议

作者： Fei-Fei Qian Nuo Xu Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Interpolation technology has evolved into a powerful tool for reversible data hiding in the image processing ***,existing interpolated algorithms only have a trivial impact on image *** this paper,an innovative interpolation and matrix-based algorithm is proposed.A novel concept of the difference between interpolated pixels is represented to dramatically improve the visual quality of the image,which lays a solid foundation for the subsequent data hiding *** is growing evidence that the Tetris matrix plays a vital role in improving embedding *** is worth mentioning that our scheme can intensely resist different attacks of various *** experimental findings demonstrate that the effect of our proposed scheme is unprecedentedly perfect even though a higher capacity is embedded than with traditional steganography approaches.

关键词： Data hiding interpolation image processing

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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Towards Information Sharing Beetle Antennae Search Optimization 24th

Towards Information Sharing Beetle Antennae Search Optimizat...

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24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Liu, Xuan Wang, Chenyan Liu, Wenjian Zhang, Lefeng Liu, Xianggan Gao, Yutong Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan China City University of Macau China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615278

The study of bioinformatics-based evolutionary computation has long been of significant interest within the scientific community. Beetle antennae search algorithm is widely used because of its lightweight, however, it lacks information sharing due to individual iteration in the algorithm. In this paper, we propose an innovative pheromone-based beetle antennae search algorithm, which evolves from a single iterative individual in BAS algorithm to multiple parallel iterative individuals and incorporates the pheromone sharing mechanism found in ant colony optimization algorithm. Applying the pheromone-based beetle antennae search algorithm to the virtual machine placement problem in cloud computing, we find that pheromone sharing mechanism allows the PB-BAS algorithm to exhibit superior optimization capabilities and effectively avoids convergence to local optimal. To verify the performance of the algorithm, we select other alternative algorithms and conduct a large number of comparative experiments under different experimental setups, the experimental results show the effectiveness and efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition 27

VANER: Leveraging Large Language Model for Versatile and Ada...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

ISBN: (纸本)9781643685489

The prevalent solution for BioNER involves using representation learning techniques combined with sequence ***, such methods are inherently task-specific, demonstrate poor generalizability, and often require a dedicated model for each *** leverage the versatile capabilities of recent large language models (LLMs), several approaches have explored generative techniques for entity ***, these approaches often fall short compared to previous sequence labeling *** this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and *** combining the LLM's understanding of instructions with sequence labeling techniques, we train a model using a mix of datasets capable of extracting various types of *** that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to enable the model to densely recognize curated *** parameter-efficient training model, VANER, significantly outperforms previous LLMs-based *** the first time, as an LLM-based model, VANER surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. © 2024 The Authors.

关键词： labeled data

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UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

UHRSNet: A Semantic Segmentation Network Specifically for Ul...

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International Conference on Pattern Recognition

作者： Lianlei Shan Minglong Li Xiaobin Li Yang Bai Ke Lv Bin Luo Si-Bao Chen Weiqiang Wang University of Chinese Academy of Sciences Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). These two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].

关键词： Training Image segmentation computer vision Semantics Graphics processing units Feature extraction Pattern recognition

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Author classification using transfer learning and predicting stars in co-author networks

Author classification using transfer learning and predicting...

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作者： Abbasi, Rashid Kashif Bashir, Ali Chen, Jianwen Mateen, Abdul Piran, Jalil Amin, Farhan Luo, Bin School of Information and Communication Engineering University of Electronics Science and Technology of China Chengdu China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Computer Science Federal Urdu University of Arts Science and Technology Islamabad Pakistan Computer Engineering Department Sejong University Yeungnam Korea Republic of Department of Information and Communication engineering Yeungnam University Yeungnam Korea Republic of Key Lab of Intelligent Computing and Signal Processing of MOE & School of Computer and Technology Anhui University China

The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning;consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods. © 2020 John Wiley & Sons Ltd

关键词： Stars

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