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TSPT: Two-Step Prompt Tuning for class-incremental novel class discovery

Knowledge-Based Systems 2025年 319卷

作者： An, Jiayu Du, Zhenbang Zhang, Herui Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

In real-world applications, models often encounter a sequence of unlabeled new tasks, each containing unknown classes. This paper explores class-incremental novel class discovery (class-iNCD), which requires maintaining previously learned knowledge while discovering novel classes. We consider a more realistic and also more challenging scenario, which has a small number of initial known classes and a large number of unlabeled tasks, with the additional requirement of data privacy protection. A simple yet effective approach, Two-Step Prompt Tuning (TSPT), is proposed. TSPT tackles class-iNCD through prompt tuning, which is rehearsal-free and plug-and-play, protecting data privacy and significantly reducing the number of trainable parameters. TSPT consists of two main steps: (1) novel class discovery, which initializes the classifier using uniform clustering, and uses intra- and inter-sample consistency learning to discover novel classes;and, (2) knowledge fusion, where the prompt learned in the previous step is adapted as task-specific prompt, and additional optimal prompts are selected from a prompt pool to integrate knowledge from both old and new classes. Experiments on three datasets demonstrated the effectiveness of TSPT. © 2025 Elsevier B.V.

关键词： Sensitive data

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Conformal Geometric Algebraic Description of Flight Collision Detection Algorithm Based on Cylinder Protection Zone 3

Conformal Geometric Algebraic Description of Flight Collisio...

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3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Wu, Haobo Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict Detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice. © 2024 IEEE.

关键词： Spheres

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Multiple-instance CNN Improved by S3TA for Colon Cancer Classification with Unannotated Histopathological images 11

Multiple-instance CNN Improved by S3TA for Colon Cancer Clas...

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11th International Conference on intelligent Control and Information processing, ICICIP 2021

作者： Ye, Tiange Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9781665425155

In this paper, we propose a new method for colon cancer classification from histopathological images, which can automatically analyze a given whole slide image (WSI). We usually classify cancer classification by referring a WSI, which is typically 20000 × 20000 pixels. The cost of obtaining WSIs with annotating cancer regions is very high. Multiple-instance learning (MIL) is a variant of supervised learning in which the instances in a bag share a single class label. That is, MIL only needs unannotated WSI. In recent years, MIL has developed a hard attention mechanism which has achieved good performance. However, this hard attention mechanism cannot notice the interior of each patch, i.e., it lacks soft attention mechanism. In this paper, a soft, sequential, spatial, top-down attention mechanism (which we abbreviate as S3TA) is used to make up for the lack of MIL attention mechanism. Finally, our experiments show that by varying the number of attention steps in S3TA, we achieved a better accuracy of 93.6% than the old model. © 2021 IEEE.

关键词： image enhancement

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Facial Recognition-Based Classroom Attendance System Utilizing Deep Learning 1

Facial Recognition-Based Classroom Attendance System Utilizi...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Zhou, Cong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

In recent years, artificial intelligence technologies, especially facial recognition, have made significant strides. With its high precision and efficiency, facial recognition has shown great potential and advantages in areas such as identity verification and behavior analysis. Classroom learning is the main channel through which students acquire knowledge, develop skills, and improve their competencies. A well-organized classroom attendance system is essential for ensuring the effective implementation and smooth execution of teaching plans. This system is based on an OpenCV and PyQt5 facial recognition client, utilizing the LBPH algorithm to achieve efficient facial recognition. In addition to providing a simple check-in function, the system offers a complete registration process and data management features. In practical use, students simply need to check in by facing the camera, allowing the system to automatically recognize the student's face and record the attendance data. This significantly simplifies the check-in process for both teachers and students, thereby improving classroom management efficiency. © 2024 IEEE.

关键词： Information management

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A new artificial immune network classifier for SAR image

A new artificial immune network classifier for SAR image

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Ruochen Niu, Manchun Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi 710071 China

ISBN: (纸本)9780819478078

Inspired by the idiotypic network theory, a new artificial immune network classifier for SAR image is proposed in this paper. In the proposed algorithm, only one B-cell instead of many B-cells is used to denote a class so as to reduce the scale of network as well as avoid the suppression operation between B-cells;moreover, a new affinity function based on the correct rate is proposed to realize antigen priority based the evaluation strategy. The proposed algorithm has been extensively compared with Fuzzy C-means (FCM), Multiple-Valued Immune Network algorithm (MVIN), and Clonal Selection Algorithm for classifier (CSA) over two SAR images. The result of experiment indicates the superiority of the algorithm over FCM, MVIN and CSA on classification accuracy and robustness. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Synthetic aperture radar

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A new constrained spectral clustering for SAR image segmentation

A new constrained spectral clustering for SAR image segmenta...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zou, Haishuang Zhou, Weida Zhang, Li Wu, Caili Liu, Ruochen Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University 710071 China

ISBN: (纸本)9781424427314

Pairwise constraints have been successful to be applied in traditional clustering methods. However, little progress has been made in incorporating them into spectral clustering. In this paper, we propose a new method to combine pairwise constraints with spectral clustering and apply it to SAR image segmentation. Firstly, we learn a distance metric using pairwise constraints. In doing so, an affinity matrix is obtained by the Gaussian function on the learned distance metric. Then we perform the spectral decomposition on the affinity matrix and we get the spectral features of data points. Finally, the constrained k-means is used to cluster spectral features instead of k-means used commonly. We apply the proposed method to synthetic aperture radar (SAR) image segmentation. Experimental results show that it is effective for SAR image segmentation. ©2009 IEEE.

关键词： image segmentation

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On-line signature verification using wavelet packet

On-line signature verification using wavelet packet

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Wang, Kaiyue Wang, Yunhong Zhang, Zhaoxiang Laboratory of Intelligent Recognition and Image Processing Beijing Key Laboratory of Digital Media Beihang University Beijing 100191 China

ISBN: (纸本)9781457713583

In this paper, we propose a novel approach for on-line signature verification using wavelet packet. Signatures are first normalized and resampled, thus they have the same number of sample points. Then, several types of local features are extracted, so that wavelet transform can be applied on them. After that, we conduct experiments to select the best local features, wavelet bases and wavelet packet settings. Also, experiments are carried out to verify the reliability and efficiency of our approach, which performs better than discrete wavelet transform and competes with the state-of-arts. © 2011 IEEE.

关键词： Discrete wavelet transforms

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Nonlinear Terminal Guidance for Hypervelocity Vehicles Impact in Specific Direction and Speed

Nonlinear Terminal Guidance for Hypervelocity Vehicles Impac...

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第25届中国控制与决策会议

作者： Kun Hu Lei Liu Yongji Wang Zhishen Wang Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Department of Control Science & Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467355322

The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.

关键词： Terminal Guidance, Nonlinear Regulator, State-dependent Riccati Equation

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Event-triggered Consensus Of Nonlinear Multi-Agent Systems With Nonlinear Interconnections

Event-triggered Consensus Of Nonlinear Multi-Agent Systems W...

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第三十二届中国控制会议

作者： YIN Xiuxia YUE Dong Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

ISBN: (纸本)9781479900305

This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.

关键词： Consensus, Multi-Agent System (MAS), Nonlinear Interconnection, Event-Triggered Control, LMI.

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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