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arXiv 2022年

作者： Peng, Juewen Cao, Zhiguo Luo, Xianrui Lu, Hao Xian, Ke Zhang, Jianming Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Adobe Research

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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A Decoupling Paradigm with Prompt Learning for Remote Sensing image Change Captioning

TechRxiv

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TechRxiv 2023年

作者： Liu, Chenyang Zhao, Rui Chen, Jianqi Qi, Zipeng Zou, Zhengxia Shi, Zhenwei Rhe Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bitemporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. Besides, for caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier’s results. The strategy can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the visual features of the feature-level encoder are fed into a frozen LLM for language generation. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://***/Chen-Yang-Liu/PromptCC © 2023, CC BY-NC-SA.

关键词： Learning systems

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Two-stage Train Components Defect Detection Based on Prior Knowledge

Two-stage Train Components Defect Detection Based on Prior K...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gang Peng Zhiyong Li Shaowei Wan Zhang Deng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan Lisai Technology Co. Ltd. Wuhan China

The existing method of detecting defects in train components, which relies on visual identification, requires extensive involvement from inspectors and presents certain limitations. In this study, a two-stage defect detection based on prior knowledge was developed, which first detects the types and positions of components, and then conducts targeted detection of possible existing defect types. The algorithm introduces the prior knowledge of the relative spatial position relationship of components and optimizes the detection of sub-components by cascaded convolutional neural networks and local scale-up. In this study, three methods were used, including deep learning, template matching, and quantitative evaluation based on prior knowledge, to perform targeted detection of defect types that may occur in components. Experiments have verified the adaptability and accuracy of the method, demonstrating its high value for engineering applications.

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Tight Formulations of Unit Commitment with General Start-Up Ramp Rate Constraints

Tight Formulations of Unit Commitment with General Start-Up ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Gao, Yijie Zhang, Xuan Yu, Yaowen Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan China Hitachi Abb Power Grid San JoseCA United States

ISBN: (纸本)9781665414395

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations. © 2021 IEEE.

关键词： Computational efficiency

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Implicit Ray-Transformers for Multi-view Remote Sensing image Segmentation

arXiv

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arXiv 2023年

作者： Qi, Zipeng Chen, Hao Liu, Chenyang Shi, Zhenwei Zou, Zhengxia The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose "Implicit Ray-Transformer (IRT)" based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images). We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations. Different from previous methods that only consider 3D prior or 2D features, we incorporate additional 2D texture information and 3D prior by broadcasting CNN features to different point features along the sampled ray. To verify the effectiveness of the proposed method, we construct a challenging dataset containing six synthetic sub-datasets collected from the Carla platform and three real sub-datasets from Google Maps. Experiments show that the proposed method outperforms the CNN-based methods and the state-of-the-art INR-based segmentation methods in quantitative and qualitative metrics. Ablation study shows that under limited label conditions, the combination of the 3D structure prior and 2D texture can significantly improve the performance and effectively complete missing semantic information in novel views. Experiments also demonstrate the proposed method could yield geometry-consistent segmentation results against illumination changes and viewpoint changes. Our dat

关键词： Remote sensing

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Prescribed Performance control of Mobile Wheeled Inverted Pendulum Systems Under Arbitrary Initial Conditions

Prescribed Performance Control of Mobile Wheeled Inverted Pe...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Mengshi Zhang Yu Cao Bo Yang Jian Huang 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 Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen China

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller.

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CLIP-guided Prototype Modulating for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Cen, Jun Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China The Hong Kong University of Science and Technology Hong Kong

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

arXiv

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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A Novel Brain-Inspired Hierarchical Perception-Association Circuit Based on Memristor Arrays

SSRN

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SSRN 2023年

作者： Chen, Liangyu Wang, Xiaoping Chen, Zhanfei Yang, Chao Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

In the brain, primary sensory cells can efficiently perceive multimodal stimuli, and then associative memory cells perform an advanced bidirectional associative memory function with perceived information. Here, a brain-inspired hierarchical perception-association circuit based on memristor arrays is proposed. Firstly, a memristive reservoir computing circuit is designed to simulate a low-level perceptual function, which mainly comprises dynamic analog reservoirs, memristor arrays, and analog integrators, enabling efficient spatio-temporal information processing. Secondly, a spiking bidirectional associative memory memristive circuit, as the core of the whole circuit, is proposed to mimick a high-level associative function, which mainly consists of memristor arrays, current amplifiers, and leaky integrate-and-fire neuron circuits, implementing multimodal associative learning. The simulation results in LTspice show that the modified neuron circuit exhibits 66× improvement in energy efficiency, and the proposed circuit can precisely perform the letter association and vision-audio association tasks in a spiking fashion with an average firing energy consumption of 230 pJ/spike, which has a great potential to be embedded in mobile robotic platforms. © 2023, The Authors. All rights reserved.

关键词： Memristors

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity 39

Topology-Aware 3D Gaussian Splatting: Leveraging Persistent ...

引用

39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

ISBN: (纸本)157735897X

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Gaussian distribution

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