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arXiv 2025年

作者： Li, Yachao Liang, Dong Ding, Tianyu Huang, Sheng-Jun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China Microsoft Washington United States

Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due to the empirical priors and illusions of these models. To address this issue, we introduce StructSR, a simple, effective, and plug-and-play method that enhances structural fidelity and suppresses spurious details for diffusion-based Real-ISR. StructSR operates without the need for additional fine-tuning, external model priors, or high-level semantic knowledge. At its core is the Structure-Aware Screening (SAS) mechanism, which identifies the image with the highest structural similarity to the low-resolution (LR) input in the early inference stage, allowing us to leverage it as a historical structure knowledge to suppress the generation of spurious details. By intervening in the diffusion inference process, StructSR seamlessly integrates with existing diffusion-based Real-ISR models. Our experimental results demonstrate that StructSR significantly improves the fidelity of structure and texture, improving the PSNR and SSIM metrics by an average of 5.27% and 9.36% on a synthetic dataset (DIV2KVal) and 4.13% and 8.64% on two real-world datasets (RealSR and DRealSR) when integrated with four state-of-the-art diffusion-based Real-ISR methods. Code - https://***/LYCEXE/StructSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Inference engines

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E²GO: Free Your Hands for Smartphone Interaction

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International Journal of Human-Computer Interaction 2025年

作者： Yan, Shaoming Ju, Yuanliang Quan, Rong Tu, Huawei Liang, Dong MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Current eye-gaze interaction technologies for smartphones are considered inflexible, inaccurate, and power-hungry. These methods typically rely on hand involvement and accomplish partial interactions. In this paper, we propose a novel eye-gaze smartphone interaction method named Event-driven Eye-Gaze Operation ((Formula presented.)), which can realize comprehensive interaction using only eyes and gazes to cover various interaction types. Before the interaction, an anti-jitter gaze estimation method was exploited to stabilize human eye fixation and predict accurate and stable human gaze positions on smartphone screens to further explore refined time-dependent eye-gaze interactions. We also integrated an event-triggering mechanism in (Formula presented.) to significantly decrease its power consumption to deploy on smartphones. We have implemented the prototype of (Formula presented.) on different brands of smartphones and conducted a comprehensive user study to validate its efficacy, demonstrating (Formula presented.) ‘s superior smartphone control capabilities across various scenarios. Demo videos. © 2025 Taylor & Francis Group, LLC.

关键词： Smartphones

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A Unified Open Adapter for Open-World Noisy Label Learning: Data-Centric and Learning-Based Insights

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zong, Chen-Chen Yang, Peng-Hui Xie, Ming-Kun Huang, Sheng-Jun Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing211106 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Tokyo103-0027 Japan

Noisy label learning (NLL) in open-world scenarios poses a novel challenge due to the presence of noisy data from both known and unknown classes. Most existing methods operate under the closed-set assumption, rendering them vulnerable to open-set noise, which significantly degrades their performance. While some approaches attempt to mitigate the impact of open-set examples, they struggle to learn effective discriminative representations for them, leading to unsatisfactory recognition performance. To address these issues, we propose a unified Open Adapter (OpenAda) that identifies open-set noise from both data-centric and learning-based perspectives, and can be easily integrated into mainstream NLL methods to improve their performance and robustness. Specifically, the data-centric part leverages label clusterability to sequentially identify basic clean and basic open-set examples both with high neighbor agreement. The learning-based part integrates one-vs-all classifiers with a progressive open disambiguation strategy to learn a reliable "inlier vs. outlier" boundary for each class. This enables the model to detect challenging open-set examples that partially overlap in the representation space with closed-set ones. Extensive experiments on synthetic and real-world datasets validate the superiority of our approach. Notably, with minor modifications, DivideMix with OpenAda achieves performance improvements of 9.31% and 18.26% on the open-world CIFAR-80 dataset under 80% symmetric noise and 40% asymmetric noise. The code is available at https://***/chenchenzong/OpenAda. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Representation Auto-fused NMF based Hierarchical Clustering

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Expert Systems with Applications 2025年 283卷

作者： Lin, Yunxia Hu, Haoyu Li, Bentian Zhao, Shuchang Jing, Hengchang School of Information Engineering Yangzhou University Yangzhou225127 China Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou225127 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Institute of Intelligent Information Processing Taizhou University Taizhou318000 China Taizhou University Taizhou318000 China

Nonnegative matrix factorization (NMF) as a powerful representation learning technique has been extensively applied in clustering tasks across diverse domains, leading to the proliferation of various NMF-based clustering methods. However, most of these methods suffer from the suboptimal performance arising from the disjointed process of representation learning and clustering, and the necessity to manually predetermine the optimal number of clusters which is a critical factor affecting the quality of clustering results. To fill this gap and inspired by convex clustering, we propose a simple yet effective NMF-based clustering method named Representation Auto-fused NMF based Hierarchical Clustering (RAFNMF-HC) by incorporating the l2 norm penalty on the differences between pairs of representations into the NMF framework to foster the automatic fusion of representations during representation learning. As the fusion continuously proceeds, similar representations gradually coalesce into small clusters, which subsequently merge into larger ones until a largest cluster encompassing all representations is formed, enabling a joint representation learning and clustering paradigm. Moreover, a cluster hierarchy as a byproduct adaptively emerges without requiring a predefined number of clusters, thus providing us with interpretability of cluster structure of data to some degree. Additionally, RAFNMF-HC only incorporates a single hyperparameter, significantly simplifying the adjustment process and boosting its practical efficiency. Our proposed RAFNMF-HC method also boasts straightforward scalability to diverse NMF variants, enhancing its versatility. Extensive experiments on various real-world datasets demonstrate the superiority of RAFNMF-HC. © 2025 Elsevier Ltd

关键词： Hierarchical clustering

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Modelling of robot attention demand in human-robot interaction using finite fuzzy state automata

Modelling of robot attention demand in human-robot interacti...

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2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012

作者： Saleh, Jamil Abou Karray, Fakhreddine Morckos, Michael Center of Pattern Analysis and Machine Intelligence University of Waterloo Canada

ISBN: (纸本)9781467315067

Many systems have been implemented towards achieving effective human-machine interaction, but run the risk of being ignored if appropriate performance metrics are not in place. As a result, our goal becomes that of providing a foundation upon which we can assess how well the human and the robot perform as a team. Toward the efficient modelling of such metrics, we attempt to determine the true amount of time that an operator has to dedicate to the robot. Therefore, we define the robot attention demand (RAD) as a function of both direct interaction time (DIT) and indirect interaction time (IIT), where the IIT is a direct consequence of the human trust in automation. We propose a two-level fuzzy temporal model to evaluate the human trust in automation while collaborating with robots to complete some tasks. The model combines the advantages of fuzzy logic and finite state machines to best model this phenomenon, and reduces the system complexity and the size of the knowledge base by grouping perceptions into first- and second-order perceptions. The fuzzy knowledge base is further updated by implementing an application robotic platform where robots and users interact via natural language to complete tasks with varying levels of complexity. User feedback is noted and used to tune the knowledge base where needed. © 2012 IEEE.

关键词： Human robot interaction

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A qualitative evaluation criterion for human-robot interaction system in achieving collective tasks

A qualitative evaluation criterion for human-robot interacti...

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2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012

作者： Saleh, Jamil Abou Karray, Fakhreddine Morckos, Michael Center of Pattern Analysis and Machine Intelligence University of Waterloo Canada

ISBN: (纸本)9781467315067

This work intends to identify common performance metrics for task-oriented human-robot interaction. We present a methodology to assess the system performance of a human-robot team in achievement of collective tasks. We propose a systematic approach that addresses the performance of both the human user and the robotic agent as a team. Toward this end, we attempt to determine the true time that an operator has to dedicate to a robot in action. We define the robot attention demand (RAD) as a function of both direct interaction time (DIT) and indirect interaction time (IIT), where the IIT is a direct consequence of the human trust in automation. We propose a two-level fuzzy temporal model to evaluate the human trust in automation while interacting with robots. Another fuzzy temporal model is presented to evaluate the human reliability during interaction time. The model is then generalized to accommodate multi-robot scenarios. Sequential and parallel robot cooperation schemes with varying levels of task dependency are considered. The fuzzy knowledge bases are further updated by implementing an application robotic platform where robots and users interact naturally to complete tasks with varying levels of complexity. User feedback is noted and used to tune the knowledge base rules where needed, to better represent a human expert's knowledge. © 2012 IEEE.

关键词： Human robot interaction

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Scene text recognition using sparse coding based features

Scene text recognition using sparse coding based features

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作者： Zhang, Dong Wang, Da-Han Wang, Hanzi Center for Pattern Analysis and Machine Intelligence Xiamen University Xiamen China

ISBN: (纸本)9781479957514

In this paper, we propose an effective scene text recognition method using sparse coding based features, called Histograms of Sparse Codes (HSC) features. For character detection, we use the HSC features instead of using the Histograms of Oriented Gradients (HOG) features. HSC features are extracted by computing sparse codes with dictionaries, which are learned from data using K-SVD, and aggregating perpixel sparse codes to form local histograms. For word recognition, we integrate multiple cues including character detection scores and geometric contexts in an objective function. The final recognition result is obtained by searching for the word which corresponds to the maximum value of the objective function. The parameters in the objective function are learned using the Minimum Classification Error (MCE) training method. Experiments on the ICDAR2003 and SVT datasets demonstrate that the HSC-based scene text recognition method outperforms the HOG-based method significantly and achieves the state-of-the-art performance. © 2014 IEEE.

关键词： Codes (symbols)

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Methods to Improve Multi-Step Time Series Prediction

Methods to Improve Multi-Step Time Series Prediction

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Koesdwiady, Arief Khatib, Alaa El Karray, Fakhri Center for Pattern Analysis and Machine Intelligence University of Waterloo C-Ontario Canada

ISBN: (纸本)9781509060146

Multi-step time series prediction is known to suffer from increasing performance degradation the farther in the future the predictions are made. In this paper, we introduce two approaches to address this weakness in recursive and multioutput prediction models. In particular, we present a model that allows recursive prediction approaches to take into account the time-step index when making predictions. In addition, we propose a conditional generative adversarial network-based data augmentation model to improve prediction performance in multioutput models. We show on real-world time series datasets that the two methods improve on multi-step time series prediction in recursive and multi-output models, respectively. © 2018 IEEE.

关键词： Forecasting

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Anomaly detection for images using auto-encoder based sparse representation 17th

Anomaly detection for images using auto-encoder based sparse...

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17th International Conference on Image analysis and Recognition, ICIAR 2020

作者： Zhao, Qiang Karray, Fakhri Center for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo Ontario Canada

ISBN: (纸本)9783030505158

Anomaly detection is a pattern recognition task that aims at distinguishing abnormal patterns from normal ones. In this paper, we propose a convolutional auto-encoder based model to detect anomaly images by producing a sparse representation in the latent space. The proposed approach is able to represent the normal images using sparse encoding and the encoding can be well reconstructed by the decoder. However, the learned convolutional filters are not able to represent the abnormal images in a sparse way. Therefore, the decoder can not reconstruct the abnormal images with high quality. By assessing the reconstruction performance, we can distinguish the abnormal images from the normal ones. The experimental results show the superiority of our proposed model over other variants of auto-encoder based anomaly detection models in terms of AUC. In addition, the results show that the sparse representation based anomaly detection method could apply to different scenarios. © Springer Nature Switzerland AG 2020.

关键词： Anomaly detection

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Vehicle localization in VANETs using data fusion and V2V communication

Vehicle localization in VANETs using data fusion and V2V com...

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2nd ACM International Symposium on Design and analysis of Intelligent Vehicular Networks and Applications, DIVANet 2012

作者： Golestan, Keyvan Seifzadeh, Sepideh Kamel, Mohamed Karray, Fakhri Sattar, Farook Center of Pattern Analysis and Machine Intelligence Department of ECE 200 University Ave. West Waterloo ON Canada

ISBN: (纸本)9781450316255

In Vehicular Ad-hoc Networks (VANETs), one of the challenging issues is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-tovehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only can assure the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures. Copyright 2012 ACM.

关键词： Vehicular ad hoc networks

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