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arXiv 2019年

作者： Xie, Qiaokang Zhou, Wengang Qi, Guo-Jun Tian, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Huawei Cloud EI Product Department Huawei Noah's Ark Laboratory

Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identities over multiple disjoint camera views. To this end, we propose a progressive deep learning method for unsupervised person Re-ID in the wild by Tracklet Association with Spatio-Temporal Regularization (TASTR). In our approach, we first collect tracklet data within each camera by automatic person detection and tracking. Then, an initial Re-ID model is trained based on within-camera triplet construction for person representation learning. After that, based on the person visual feature and spatio-temporal constraint, we associate cross-camera tracklets to generate cross-camera triplets and update the Re-ID model. Lastly, with the refined Re-ID model, better visual feature of person can be extracted, which further promote the association of cross-camera tracklets. The last two steps are iterated multiple times to progressively upgrade the Re-ID model. To facilitate the study, we have collected a new 4K UHD video dataset named Campus-4K with full frames and full spatio-temporal information. Experimental results show that with the spatio-temporal constraint in the training phase, the proposed approach outperforms the state-of-the-art unsupervised methods by notable margins on DukeMTMC-reID, and achieves competitive performance to fully supervised methods on both DukeMTMC-reID and Campus-4K datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

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IEEE International Conference on Image processing

作者： Yue Li Li Li Zhu Li Jianchao Yang Ning Xu Dong Liu Houqiang Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China University of Missouri Kansas City MO USA Snapchat Inc. CA USA

For chroma intra prediction, previous methods exemplified by the Linear Model method (LM) usually assume a linear correlation between the luma and chroma components in a coding block. This assumption is inaccurate for complex image content or large blocks, and restricts the prediction accuracy. In this paper, we propose a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network. Specifically, we utilize a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network to extract features from the neighboring reconstructed luma and chroma samples. The extracted twofold features are then fused to predict the chroma samples-Cb and Cr simultaneously. The proposed chroma intra prediction method is integrated into HEVC. Preliminary results show that, compared with HEVC plus LM, the proposed method achieves on average 0.2%, 3.1% and 2.0% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration.

关键词： Feature extraction Neural networks Kernel Correlation Image reconstruction Encoding Prediction methods

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A3Net: Adversarial-and-attention network for machine reading comprehension

arXiv

引用

arXiv 2018年

作者： Wang, Jiuniu Fu, Xingyu Xu, Guangluan Wu, Yirong Chen, Ziyan Wei, Yang Jin, Li Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics CAS Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%). Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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Prior-enlightened and motion-robust video deblurring

arXiv

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arXiv 2020年

作者： Zhou, Ya Xu, Jianfeng Tasaka, Kazuyuki Chena, Zhibo Li, Weiping CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China KDDI Research Inc. Fujimino-shi Saitama356-8502 Japan

Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed. Most existing works have strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information, explicitly enhancing the scenes’ perception while mitigating the output’s artifacts. On the other hand, we design the priors representing blur distribution, to better handle non-uniform blur in spatio-temporal domain. Besides the classical camera shake caused global blurry, we also prove the generalization for the downstream task suffering from local blur. Extensive experiments demonstrate we can achieve the state-of-the-art performance on well-known REDS and GoPro datasets, and bring machine task gain. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

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DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REMOTE SENSING IMAGE INPAINTING

DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REM...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Daoyu Lin Guangluan Xu Yang Wang Xian Sun Kun Fu School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Network) can alleviate the vanishing-gradient problem, strengthen feature reuse, and substantially reduce the memory usage. We apply residual learning to learn the mappings from corrupted image to recovered image directly;it will back-propagate gradient to the bottom layers and accelerate the training process. We train the proposed Dense-Add Net with a robust Charbonnier loss function which can achieve high-quality reconstruction. The experimental verify the efficacy of our proposed Dense-Add Net.

关键词： Remote sensing Training Acceleration Image reconstruction Painting Convolution Interpolation

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AN ANISOTROPIC SCATTERING ANALYSIS METHOD BASED ON LIKELIHOOD RATIO USING CIRCULAR SAR DATA

AN ANISOTROPIC SCATTERING ANALYSIS METHOD BASED ON LIKELIHOO...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fei Teng Wen Hong Yun Lin Bing Han Yanping Wang Wenjie Shen Shanshan Feng Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China North China University of Technology Beijing China

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method.

关键词： Apertures Image color analysis Radar polarimetry Trajectory Optical imaging Optical scattering

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Exploration of Image Search Results Quality Assessment

引用

IEEE Transactions on Big Data 2015年第3期1卷 95-108页

作者： Tian, Xinmei Lu, Yijuan Stender, Nate Yang, Linjun Tao, Dacheng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230027 China Department of Computer Science Texas State University San MarcosTX78666 United States Department of Computer Science University of Nebraska-Lincoln LincolnNE68588 United States Microsoft Corporation RedmondWA98052-7329 United States Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper aims to solve this problem and makes four main innovations. First, a preference learning model is proposed to quantitatively study and formulate the best image search result list identification problem. Second, a set of valuable preference learning related features is proposed by exploring the visual characters of returned images. Third, a query-dependent preference learning model is further designed for building a more precise and query-specific model. Fourth, the proposed approach has been tested on a variety of applications including reranking ability assessment, optimal search engine selection, and synonymous query suggestion. Extensive experimental results on three image search datasets demonstrate the effectiveness and promising potential of the proposed method. © 2015 IEEE.

关键词： Image retrieval

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One-shot Generative Domain Adaptation in 3D GANs

arXiv

引用

arXiv 2024年

作者： Li, Ziqiang Wu, Yi Wang, Chaoyue Rui, Xue Li, Bin Nanjing University of Information Science and Technology Nanjing China University of Science and Technology of China Hefei China University of Sydney Australia CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://***/iceli1007/3D-Adapter. © 2024, CC BY.

关键词： Generative adversarial networks

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Preliminary Analysis of PolInSAR Scattering Mechanism in Urban Areas through UAV-borne PolInSAR system

Preliminary Analysis of PolInSAR Scattering Mechanism in Urb...

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IEEE International Conference on Radar

作者： Zexin Lv Xiaolan Qiu Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Dara Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China

ISBN: (纸本)9781665468893

Polarimetric Interferometric SAR (PolInSAR) can improve the coherence of images and it plays an important role in urban remote sensing. The explanation of its scattering mechanism is concerned by many researchers. It is always explained from the aspect of different polarization decompositions but seldom from the height of scattering centers. In this paper, a Ku-band UAV-borne PolInSAR system is used to compare the relationship of scattering centers between PolInSAR and Pauli decomposition on different artificial targets. The physical meaning of PolInSAR is preliminary analyzed and our conclusion is also verified through a C-band manned airborne PolInSAR system.

关键词： C-band Airborne radar Urban areas Scattering Coherence Radar polarimetry Synthetic aperture radar

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SFDE-net: A spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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