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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yuting Shi Yin Zhuang He Chen Miaoxin Cai Jianlin Xie Shuyu Gan National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Multi-object tracking (MOT) in satellite videos is a vital technique in Earth observation, serving various purposes such as smart city management and military target reconnaissance. However, satellite videos face several challenges, including wide observation ranges, low target pixel proportions, and significant background interference such as cloud drift and lighting changes. Relying solely on appearance features from single-frame videos is insufficient for fully identifying targets; extracting target motion information across multiple frames is also necessary for *** paper introduces an innovative integrated real-time architecture for multi-object tracking, named DCTracker. Specifically, to address the loss of target motion information and the issue of weak targets being easily obscured by noise, we designed a differential compensation module to enhance discrimination between targets and noise by analyzing the motion characteristics of targets across consecutive video frames. Experimental results show that DCTracker achieves advanced performance, with an accuracy score of 61.3% on the VISO dataset, demonstrating its effectiveness in handling complex tracking scenarios.

关键词： Target tracking Satellites Smart cities Noise Semantics Reconnaissance Feature extraction Spatiotemporal phenomena Remote sensing Videos

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Interpretable Object Recognition by Semantic Prototype Analysis

Interpretable Object Recognition by Semantic Prototype Analy...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Qiyang Wan Ruiping Wang Xilin Chen Institute of Computing Technology CAS Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Beijing China University of Chinese Academy of Sciences Beijing China

People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a recognition model with such principles to explain the recognition process to enhance human trust. We propose Semantic Prototype Analysis Network (SPANet), an interpretable object recognition approach that enables models to explicate the decision process more lucidly and comprehensibly to humans by "pointing out where to focus" and "telling about why it is" simultaneously. With the proposed method, some part prototypes with semantic concepts will be provided to elaborate on the classification together with a group of visualized samples to achieve both part-wise and semantic interpretability. The results of extensive experiments demonstrate that SPANet is able to recognize objects almost as well as the non-interpretable models, at the same time generating intelligible explanations for its decision process.

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Building Semantic Segmentation Through an Ensemble Architecture of UNet and Graph Convolution Networks 6

Building Semantic Segmentation Through an Ensemble Architect...

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6th International Conference on Electronic information technology and Computer Engineering, EITCE 2022

作者： Shang, Yuxin Cui, Hao Guo, Haitao Shandong Institute of Commerce and Technology Shandong Province Jinan China Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing Shandong Province Dongying China

ISBN: (纸本)9781450397148

Extracting building from remote sensing images remains a challenge because some obstacles, such as trees, cars, farmland, and shadows, disturb the identification accuracy. In recnet years, deep convolution neural networks have shown significant improvements in computer vision, however it is still difficult to extract irregular and small buildings. To develop the extraction accuracy of building, we propose a deep learning model, which combined UNet and graph convolution network, to capture the long-range information. Seven deep convolution neural networks were used on the Massachusetts Buildings Dataset for comparison. And the proposed model, which performed best among these compared models, achieved a building Intersection over Union (IOU) of 71.10%, mean IOU of 81.88%, recall of 81.90%, precision 84.36% and F1 score of 83.11%. This result indicated that the proposed model can accurately extract building objects. The visualization results showed that the proposed model performs better in extracting irregular and small buildings and caneffectively extracts buildings from high-resolution remote-sensing images. © 2022 Association for computing Machinery.

关键词： Convolution

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Revolutionizing Antibody Discovery:An Innovative AI Model for Generating Robust Libraries

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 910-912页

作者： Yaojun Wang Shiwei Sun College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100080China University of Chinese Academy of Sciences Beijing 100049China

Background Artificial intelligence models of natural language are becoming increasingly adept at processing and‘understanding’*** are widely used in applications such as automated speech recognition,translation,smart assistants,and text generation[1].Recently,language models have been applied to such problems as protein function prediction,protein evolution analysis,and protein design[2].

关键词： becoming smart language.

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Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting

arXiv

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arXiv 2024年

作者： Pei, Gaozheng Lyu, Shaojie Ma, Ke Yang, Pinci Xu, Qianqian Sun, Yingfei School of Electronic Electrical and Communication Engineering UCAS Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Tencent Corporate

Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model’s structure, parameters, or training data. The adversary can only access the target model’s predictions for generated samples. Once the substitute model closely approximates the behavior of the target model, attackers can exploit its white-box characteristics for subsequent malicious activities, such as adversarial attacks. Existing methods within cooperative game frameworks often produce samples with high confidence for the prediction of the substitute model, which makes it difficult for the substitute model to replicate the behavior of the target model. This paper presents a new data-free model stealing approach called Query Efficient Data Generation (QEDG). We introduce two distinct loss functions to ensure the generation of sufficient samples that closely and uniformly align with the target model’s decision boundary across multiple classes. Building on the limitation of current methods, which typically yield only one piece of supervised information per query, we propose the query-free sample augmentation that enables the acquisition of additional supervised information without increasing the number of queries. Motivated by theoretical analysis, we adopt the consistency rate metric, which more accurately evaluates the similarity between the substitute and target models. We conducted extensive experiments to verify the effectiveness of our proposed method, which achieved better performance with fewer queries compared to the state-of-the-art methods on the real MLaaS scenario and five datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query Language

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ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations

ES3: Evolving Self-Supervised Learning of Robust Audio-Visua...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanhang Zhang Shuang Yang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a novel strategy, ES 3 , for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video, we reframe the problem as the acquisition of shared, unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation, we propose a novel “evolving” strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First, we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next, we incorporate video-unique speech information and bootstrap the audio-visual repre-sentations on top of the previously acquired shared knowledge. Finally, we maximize the total audio-visual speech information, including synergistic information to obtain robust and comprehensive representations. We implement ES 3 as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular, on LRS2-BBC, our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

关键词： Visualization Computer vision Self-supervised learning Manuals Benchmark testing Data models Pattern recognition

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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Multi-View Consistent 3D GAN Inversion via Bidirectional Encoder

Multi-View Consistent 3D GAN Inversion via Bidirectional Enc...

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International Conference on Automatic Face and Gesture Recognition

作者： Haozhan Wu Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of the Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

3D GAN inversion enables not only 3D reconstruction from a 2D image, but also novel view synthesis and image editing. Existing works ensure the novel view synthesis quality by constraining the synthesized views to conform to the real image distribution. However, most of the methods did not consider the multi-view consistency, i.e., different photos of the same 3D scene via 3D GAN inversion can be inverted to the same 3D scene. In this paper, we propose a bidirectional encoder (BiDiE) for 3D GAN inversion that can improve the multi-view consistency and alleviate the interference of camera parameter prediction errors. On the one hand, the bidirectional encoder takes real images as input, estimates the camera parameters, and performs 3D reconstruction. On the other hand, the bidirectional encoder takes randomly sampled latent code and camera parameters as input, and generates synthesized images to assist in the latent code learning process. In addition, we extend the latent space from W+ to W++ to improve its reconstruction and editing capabilities. Experiments on the FFHQ, CelebA-HQ and Multi-PIE datasets prove that our proposed method outperforms state-of-the-art methods in multi-view consistent reconstruction as well as editing capability. 1 1 Code and datasets are available at https://***/WHZMM/BiDiE

关键词： Three-dimensional displays Codes Face recognition Interference Gesture recognition Cameras Image reconstruction

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on intelligent computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation

TransCGAN-based Parameter Extraction Framework for SAR Image...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Sidan Deng Jingfei He Yongfei Mao Liangbo Zhao Liang Chen Hao Shi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing & Chongqing Innovation Center Beijing Institute of Technology Chongqing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing & Chongqing Innovation Center Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images.

关键词： Training Accuracy Imaging Radar imaging Transformers Generative adversarial networks Radar polarimetry Software Synthetic aperture radar Parameter extraction

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