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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heyuan Yin Jingxiang Yang Jia Liu Liang Xiao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems The Ministry of Industry and Information Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST.

关键词： Degradation Three-dimensional displays Computational modeling Imaging Transformer cores Signal processing Transformers Speech processing Image reconstruction Hyperspectral imaging

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United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images

arXiv

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arXiv 2024年

作者： Sun, Yanguang Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: https://***/CSYSI/UDCNet. © 2024, CC BY.

关键词： Optical remote sensing

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Context-Aware Aerial Object Detection: Leveraging Inter-Object and Background Relationships

arXiv

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arXiv 2024年

作者： Ren, Botao Xu, Botian Yang, Xue Pu, Yifan Wang, Jingyi Deng, Zhidong Tsinghua University China OpenGVLab Shanghai AI Laboratory China Department of Computer Science State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China

In most modern object detection pipelines, the detection proposals are processed independently given the feature map. Therefore, they overlook the underlying relationships between objects and the surrounding background, which could have provided additional context for accurate detection. Because aerial imagery is almost orthographic, the spatial relations in image space closely align with those in the physical world, and inter-object and object-background relationships become particularly significant. To address this oversight, we propose a framework that leverages the strengths of Transformer-based models and Contrastive Language-Image Pre-training (CLIP) features to capture such relationships. Specifically, Building on two-stage detectors, we treat Region of Interest (RoI) proposals as tokens, accompanied by CLIP Tokens obtained from multi-level image segments. These tokens are then passed through a Transformer encoder, where specific spatial and geometric relations are incorporated into the attention weights, which are adaptively modulated and regularized. Additionally, we introduce self-supervised constraints on CLIP Tokens to ensure consistency. Extensive experiments on three benchmark datasets demonstrate that our approach achieves consistent improvements, setting new state-of-the-art results with increases of 1.37 mAP50 on DOTA-v1.0, 5.30 mAP50 on DOTA-v1.5, 2.30 mAP50 on DOTA-v2.0 and 3.23 mAP50 on DIOR-R. © 2024, CC BY.

关键词： Object detection

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Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

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IEEE International Conference on Electro-Information technology

作者： John Knudson Adriano Cavalcanti Maninder Singh Department of Computer Science and Information Technology Autonomous Systems Laboratory St. Cloud State University St. Cloud MN USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The article explores the bleeding-edge intersection of neuro-haptics, brain-computer interface (BCI) technology, and machine learning to enable the creation and control of flying avatars. Our work integrates neuroscience and haptic feedback to develop human computer system interfaces that allow users to control cyber physical systems using their brain signals. This innovative approach uses advanced machine learning to classify signal patterns and respond to brain thoughts in real-time, providing an immersive flying bird’s eyes feeling. The article highlights the challenges and opportunities associated with Neuro-Haptics BCI for flying avatars, paving the way for a new era in human-computer interaction.

关键词： Human computer interaction Training Neuroscience Avatars Machine learning Brain-computer interfaces Real-time systems

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DEAMP: Dominant-Eye-Aware Foveated Rendering with Multi-Parameter optimization

DEAMP: Dominant-Eye-Aware Foveated Rendering with Multi-Para...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Zhimin Wang Xiangyuan Gu Feng Lu State Key Laboratory of VR Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

The increasing use of high-resolution displays and the demand for interactive frame rates presents a major challenge to widespread adoption of virtual reality. Foveated rendering address this issue by lowering pixel sampling rate at the periphery of the display. How-ever, existing techniques do not fully exploit the feature of human binocular vision, i.e., the dominant eye. In this paper, we propose a Dominant-Eye-Aware foveated rendering method optimized with Multi-Parameter foveation (DEAMP). Specifically, we control the level of foveation for both eyes with two distinct sets of foveation parameters. To achieve this, each eye’s visual field is divided into three nested layers based on eccentricity. Multiple parameters govern the level of foveation of each layer, respectively. We conduct user studies to evaluate our method. Experimental results demonstrate that DEAMP is superior in terms of rendering time and reduces the disparity between pixel sampling rate and the visual acuity fall-off model while maintaining the perceptual quality.

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Cumulus cloud modeling from images based on VAE-GAN

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Virtual Reality & Intelligent Hardware 2021年第2期3卷 171-181页

作者： Zili ZHANG Yunchi CEN Fan ZHANG Xiaohui LIANG State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Shijiazhuang UniversityShijiazhuang 050035China

Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to solve this *** of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development *** In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single *** method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial ***,a 3D cloud shape is mapped into a unique hidden space using the proposed ***,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered *** train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus *** cumulus clouds were rendered under different lighting *** The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing ***,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction *** The proposed autoencoder network learns the latent space of 3D cumulus cloud *** presented reconstruction architecture models a cloud from a single *** demonstrated the effectiveness of the two models.

关键词： 3D cloud model 3D autoencoder network Generative adversarial network

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Hybrid Scenario Generation Method for Stochastic Virtual Bidding in Electricity Market

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CSEE Journal of Power and Energy systems 2021年第6期7卷 1312-1321页

作者： Dongliang Xiao Wei Qiao the School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China the Power and Energy Systems Laboratory Department of Electrical and Computer EngineeringUniversity of Nebraska-LincolnLincolnNE 68588-0511 USA

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using *** paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical *** electricity price spikes are first identified by using an outlier detection ***,the historical data are decomposed into base and spike ***,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,***,the electricity price scenarios are obtained by combining the base and spike component *** studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed *** optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,*** study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.

关键词： Electricity market electricity price scenario generation stochastic optimization virtual bidding

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Weed Detection and Treatment by RGB System - Case of the FPL Smart Farm 23

Weed Detection and Treatment by RGB System - Case of the FPL...

引用

6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Laftouty, Zineb Bouraada, Khalid Bakrim, Ahmed Boudhir, Anouar Abdelhakim Essafi, Mariam Abdelmalek Essaadi University Tetouan Morocco Smart Systems and Emerging Technologies Research Team Computer Sciences and Smart Systems Laboratory Faculty of Science and Technology of Tangier Abdelmalek Essaadi University Tetouan Morocco Biology Department Dhar El Mehraz Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (纸本)9798400700194

Strawberry is one of the most important crops in the Loukkos area in Morocco;it exceeds 80% of the national production. This crop is very sensitive and requires well-controlled climatic conditions, specific inputs and meticulous monitoring to avoid any uncertainty in yield. Our work consists in treating the side related to the weeds, which can cause considerable losses of yield and disturb plant development. The danger of these weeds in this culture is the strong competition with the needs of the culture (water, nutrients, light...). The objective of our work is to make a state of the art by bibliographic systemic analysis and propose intelligent methods for detection and treatment of weeds based on RGB images. Most of the works are based on image acquisition mainly by smartphones or unmanned aerial vehicles (UAVs), and some by professional cameras. The image processing can be done by deep learning: through convolutional neural networks (CNN), support vector machine (SVM), or local binary pattern (LBP). Other methods mentionned allow detecting and controlling weeds using a robot that determines the undesirable plants by image processing (via deep learning), records their location and proceeds to a weeding or a localized treatment using herbicides. These methods allow decreasing labor dependency, a better treatment efficiency, and a lower herbicide use up to 90%. We thus propose a best suitable method to detect and treat weeds in an intelligent way in the "Smart Farm"of the Polydisciplinary Faculty in Larache. This method consists in using fluorescent paint at the stem base of the strawberry plant in order to differentiate the main crop from any undesirable plant. The robot that takes RGB images under UV rays considers as "weed"any plant that does not contain fluorescent paint. © 2023 ACM.

关键词： Weed control

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