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International Conference on computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

arXiv

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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

关键词： Object detection

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Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

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Nuclear science and Techniques 2022年第3期33卷 62-76页

作者： Kai Chen Li-Bo Zhang Jia-Shun Liu Yuan Gao Zhan Wu Hai-Chen Zhu Chang-Ping Du Xiao-Li Mai Chun-Feng Yang Yang Chen Southeast University Nanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 210096China Department of Radiology General Hospital of the Northern Theater of the Chinese People’s Liberation ArmyShenyang 110016China Laboratory of Image Science and Technology The School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China Department of Radiology Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjing 210008China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of RadiologyZhongda HospitalSoutheast UniversityNanjing 210009China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke *** widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion *** the dose of X-rays causes severe noise and artifacts in PCT *** solve this problem,we propose a deep learning method called *** exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into *** decomposes the convolved features into high-and low-frequency *** decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image *** Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent *** extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact *** and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

关键词： Cerebral perfusion CT Low-dose Image denoising Perfusion parameters

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RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing Imagery using a Cross Semantic Attribute-guided Transformer

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Damalla Rambabu Swetha G Rajeshreddy Datla Vishnu Chalavadi C Krishna Mohan Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Dept. of Space Advanced Data Processing Research Institute (ADRIN) Secunderabad India Indian Institute of Technology Tirupati India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot learning (ZSL) based scene classification aims to recognize unseen classes by transferring semantic information from seen classes. The applicability of ZSL for scene classification in remote sensing images becomes challenging due to the complexity of scenes. Earlier attention-based methods are ineffective for extracting discriminative region-based features within a single image. This limitation hinders their ability to achieve transferability and accurately localize object attributes, which is essential for extracting discriminative region-based features. Hence, we propose a method for zero-shot scene classification in remote sensing images using a cross-semantic attribute-guided Transformer named RSZero-CSAT. Firstly, the semantic information is acquired using shared remote sensing semantic attributes to localize object attributes that characterize discriminative region features. Then, a Transformer in RSZero-CSAT is employed to localize object attributes within visual features accurately, enhancing the effectiveness of semantic information transfer in ZSL. Specifically, the RSZero-CSAT employs a semantic attribute → visual Transformer (SAVT) and a visual → semantic attribute Transformer (VSAT) components to extract visual features guided by semantic attributes and semantic attribute features guided by visual features, respectively. Further, SAVT and VSAT mutually learn and collaborate to obtain semantically enriched visual representations, leveraging prediction-level and feature-level semantic collaborative losses for capturing crucial semantic information. Finally, the semantically enriched visual representations obtained from SAVT and VSAT are combined to facilitate visual-semantic interactions in collaboration with class semantic vectors to classify ZSL. Our experimental results demonstrate the impact of RSZero-CSAT in improving the performance of unseen classes on four scene classification benchmark datasets in remote sensing images. The code is avai

关键词： Visualization Scene classification Zero-shot learning Semantics Collaboration Benchmark testing Feature extraction

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Facial Expression Recognition Using Transfer Learning with ResNet50 7th

Facial Expression Recognition Using Transfer Learning with R...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Hiremath, Shantala S. Hiremath, Jayaprada Kulkarni, Vaishnavi V. Harshit, B.C. Kumar, Sujith Hiremath, Mrutyunjaya S. Image Processing Sony India Software Centre Pvt. Ltd. Karnataka Bangalore560103 India Department of Computer Science and Engineering Visvesvaraya Technological University Belgaum590018 India Department of CSE Alvas Institute of Engineering and Technology Mijar Karnataka Moodbidri574225 India Department of Image Processing eMath Technology Pvt. Ltd. Karnataka Bangalore560072 India Artificial Intelligence and Machine Learning VVDN Technologies Pvt. Ltd. Karnataka Bangalore560066 India

ISBN: (纸本)9789819916238

Facial expression recognition mimics human coding abilities and delivers non-verbal human–robot communication cues. Machine learning and deep learning techniques enable real-world computer vision applications. Deep learning-based facial emotion recognition models have under-fitted or over-fitted due to inadequate training data. They are using FER2013’s 7 picture categories. Face detection using AdaBoost, scaling with OpenCV, and contrast improvement with histogram equalization preprocess these pictures. These pre-processed pictures are given to the ResNet50 pre-trained network, which obtained 77.3% accuracy. Transfer learning improves this outcome. By running pre-processed pictures through ResNet50’s FC1000 layer, features are retrieved and trained using a multiclass nonlinear support vector machine (SVM) classifier with seven classes. Training with 89.923% accuracy creates a knowledge base. Emotion recognition techniques let robots understand people, which can improve HCI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Multi-Scale Local and Global Feature Fusion for Blind Quality Assessment of Enhanced Images

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Cao, Jingchao Zhang, Shuai Liu, Yutao Gao, Feng Gu, Ke Zhai, Guangtao Dong, Junyu Kwong, Sam Ocean University of China School of Computer Science and Technology Qingdao266100 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Lingnan University Department of Computing and Decision Science Hong Kong

Image enhancement plays a crucial role in computer vision by improving visual quality while minimizing distortion. Traditional methods enhance images through pixel value transformations, yet they often introduce new distortions. Recent advancements in deep learning-based techniques promise better results but challenge the preservation of image fidelity. Therefore, it is essential to evaluate the visual quality of enhanced images. However, existing quality assessment methods frequently encounter difficulties due to the unique distortions introduced by these enhancements, thereby restricting their effectiveness. To address these challenges, this paper proposes a novel blind image quality assessment (BIQA) method for enhanced natural images, termed multi-scale local feature fusion and global feature representation-based quality assessment (MLGQA). This model integrates three key components: a multi-scale Feature Attention Mechanism (FAM) for local feature extraction, a Local Feature Fusion (LFF) module for cross-scale feature synthesis, and a Global Feature Representation (GFR) module using Vision Transformers to capture global perceptual attributes. This synergistic framework effectively captures both fine-grained local distortions and broader global features that collectively define the visual quality of enhanced images. Furthermore, in the absence of a dedicated benchmark for enhanced natural images, we design the Natural Image Enhancement Database (NIED), a largescale dataset consisting of 8,581 original images and 102,972 enhanced natural images generated through a wide array of traditional and deep learning-based enhancement techniques. Extensive experiments on NIED demonstrate that the proposed MLGQA model significantly outperforms current state-of-the-art BIQA methods in terms of both prediction accuracy and robustness. © 1991-2012 IEEE.

关键词： Image quality

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GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling

arXiv

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arXiv 2024年

作者： Ju, Wei Gu, Yiyang Mao, Zhengyang Qiao, Ziyue Qin, Yifang Luo, Xiao Xiong, Hui Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University Beijing100871 China The Hong Kong University of Science and Technology Guangzhou511453 China Department of Computer Science University of California Los Angeles90095 United States

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views (i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named Graph Pooling ContraSt (GPS) to address these issues. Motivated by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

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computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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Heterogeneous Laplace Distribution Noise Background Modeling for Sperm Detection in Microscopic Images 9

Heterogeneous Laplace Distribution Noise Background Modeling...

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9th International Conference on computer and Communications, ICCC 2023

作者： Tian, Manqing Zhao, Yang Zeng, Ni Kang, Xiaomei Pei, Jihong Yang, Xuan Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Huazhong University of Science and Technology Tongji Medical College Department of Reproductive Maternal and Child Health Hospital of Hubei Province Wuhan China

ISBN: (纸本)9798350317251

Semen quality assessment is a very important tool for the timely detection and treatment of infertility disorders. Many artificial intelligence methods for sperm image analysis have emerged in recent years. Sperm detection in sperm microscopy is the first step in semen quality assessment. However, the acquisition of sperm electron microscopy images can produce a variety of different background lighting situations. The performance of established methods is susceptible to changes in background illumination. Therefore, a sperm detection method with heterogeneous Laplacian distribution noise background modeling (HLDNBM) is proposed. In this paper the method uses background illumination surface fitting to eliminate the effect of illumination in a single sperm image, and realizes the segmentation of sperm head and sperm tail with different grayscale by upper and lower segmentation threshold surfaces to achieve complete sperm image segmentation and multi-target detection in a single image. This method effectively avoids the degradation of target detection performance caused by sudden changes in illumination in other background modeling methods. Moreover, this method shows strong adaptability for images with different lighting backgrounds, and is friendly to small datasets as well. The experimental results show that the accuracy and sensitivity of the algorithm for sperm detection in images with different grayscale characteristics are very promising, with high universality and robustness. © 2023 IEEE.

关键词： Lighting

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