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arXiv 2018年

作者： He, Anfeng Luo, Chong Tian, Xinmei Zeng, Wenjun CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China Microsoft Research Beijing China

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC [3] allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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User specific friend recommendation in social media community

User specific friend recommendation in social media communit...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cong Guo Xinmei Tian Tao Mei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China Microsoft Research Beijing China

Social networks nowadays have become an important form of communication in which users can post their current status or share their lives by mobile phones or the Web. In this paper, we develop an effective and efficient model to estimate continuous tie strength between users for friend recommendation with the heterogeneous data from social media community. We categorize those multimodal data into two classes: interaction data (e.g., comments, marking favorite photos) and similarity data(e.g., common friends, groups, tags, geo, visual). We propose to use asymmetric relationship in the interaction data for tie strength estimation instead of using the conventional symmetric ones. Furthermore, by exploring the behavior of users in a social media community, we find that the tie strength between users can be approximately modeled as a linear function of their social connections. Based on this observation, we propose an effective and highly efficient user specific linear model for the tie strength estimation. The experiments on a popular social network show promising results and demonstrate the effectiveness of our proposed method.

关键词： Kernel Social network services Visualization Communities Data models Estimation Vectors

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CONVOLUTIONAL NEURAL NETWORK-BASED ARITHMETIC CODING OF DC COEFFICIENTS FOR HEVC INTRA CODING

CONVOLUTIONAL NEURAL NETWORK-BASED ARITHMETIC CODING OF DC C...

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IEEE International Conference on Image processing

作者： Changyue Ma Dong Liu Xiulian Peng Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.

关键词： Context modeling Probability distribution Entropy coding Codecs Training data Tools

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Convolutional Neural Network-Based Invertible Half-Pixel Interpolation Filter for Video Coding

Convolutional Neural Network-Based Invertible Half-Pixel Int...

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IEEE International Conference on Image processing

作者： Ning Yan Dong Liu Houqiang Li Tong Xu Feng Wu Bin Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

Fractional-pixel interpolation has been widely used in the modern video coding standards to improve the accuracy of motion compensated prediction. Traditional interpolation filters are designed based on the signal processing theory. However, video signal is non-stationary, making the traditional methods less effective. In this paper, we reveal that the interpolation filter can not only generate the fractional pixels from the integer pixels, but also reconstruct the integer pixels from the fractional ones. This property is called invertibility. Inspired by the invertibility of fractional-pixel interpolation, we propose an end-to-end scheme based on convolutional neural network (CNN) to derive the invertible interpolation filter, termed CNNInvIF. CNNlnvIF does not need the “ground-truth” of fractional pixels for training. Experimental results show that the proposed CNNInvIF can achieve up to 4.6% and on average 2.2% BD-rate reduction than HEVC under the low-delay P configuration.

关键词： Interpolation Video coding Training Image reconstruction Geometry Convolutional neural networks Image coding

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Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

arXiv

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arXiv 2020年

作者： Wang, Ning Zhou, Wengang 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 Hefei China Huawei Noah’s Ark Laboratory

Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, which deserve additional attention in online tracking and model update. To enhance the tracking robustness, in this paper, we propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates via an efficient convolutional regression. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning. During the model update, we utilize the hard negative mining technique and an adaptive ridge regression scheme to improve the discrimination capability of the second-stage regressor. Extensive experiments are conducted on 11 challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019, UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed method achieves state-of-the-art performance on prevalent benchmarks, while running in a real-time speed. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Self-guided Few-Shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models 8th

Self-guided Few-Shot Semantic Segmentation for Remote Sensi...

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8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Qi, Xiyu Wu, Yifan Mao, Yongqiang Zhang, Wenhui Zhang, Yidan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031717154

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM’s dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided mask to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underlines the superiority of our approach, outperforming other available few-shot methodologies. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Semantic Segmentation

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Research on Characteristics of Cylinder Tanks in SAR Image

Research on Characteristics of Cylinder Tanks in SAR Image

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2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

作者： Yueting Zhang Hongzhen Chen Kan Tang Wenchang Xiong Chibiao Ding Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of SciencesBeijing China The National Key Laboratory of Microwave Imaging Technology Beijing China Institute of Electronics Chinese Academy of Science

Cylinder tanks appear as arcs in SAR image. It is found the radius of some arcs is bigger than the radius of the tank. To analysis this phenomenon, combining the scattering theory and the principle of SAR, a novel prediction model about the SAR image of cylinder tanks, including the tanks with fixed top and floating top, is proposed. Analysis on details of the SAR image of the tanks is provided by the prediction model. Several experiments are implemented.

关键词： Cylinder tanks SAR image geometry extraction.

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3D Point Cloud Reconstruction of Vehicle using Inversely Mapping and Voting from CSAR Images

3D Point Cloud Reconstruction of Vehicle using Inversely Map...

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IEEE International Conference on Radar

作者： Shanshan Feng Yun Lin Fei Teng Wen Hong Key Laboratory of Technology in Geospatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665468893

3D reconstruction of object has raised much interest in the field of SAR. The feature of target at multi aspect angles can be obtained from sub-aperture images provided by circular SAR(CSAR), which is conducive to 3D reconstruction. Radargrammetric method is a conventional way to extract DEM while its performance for 3D reconstruction is limited by the image registration. It is difficult to find corresponding points in different sub-aperture images. In this paper, we proposed a method for 3D reconstruction of vehicle based on projections in sub-aperture images. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell. For a projection point, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidates of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set.

关键词： Point cloud compression Space vehicles Image registration Three-dimensional displays Imaging Radar imaging Feature extraction

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Model selection for high resolution InSAR coherence statistics over urban areas and its application in building detection

Model selection for high resolution InSAR coherence statisti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yue Zhang Xian Sun Wenhui Diao Chenyuan Wang Guangluan Xu Hongqi Wang Institute of Electronic Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

The interferometric coherence map is derived from the cross-correlation of two registered synthetic aperture radar (SAR) images. It can give additional information complementary to the intensity image, or act as an independent information source in many applications. Compared to the plenty of work on SAR intensity statistics, there are quite fewer researches on the statistical characters of interferometric SAR (InSAR) coherence. And to our knowledge, all of the existing work that related to InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. Our main contribution is the investigation on the accuracies of several typical models for high resolution coherence statistics over urban areas. We select three typical land classes including trees, buildings, and shadow, as the representatives of urban areas. And different models including Gaussian, Weibull, Rayleigh, Nakagami and Beta are evaluated. Experiment results on TanDEM-X data illustrate that the Beta model reveals a better performance than other distributions. Finally, the Beta model is used in the detection of buildings.

关键词： Coherence Buildings Synthetic aperture radar Urban areas Probability density function Data models Nakagami distribution

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Identity Regularized Sparse Representation for Automatic Target Recognition in Sar Images

Identity Regularized Sparse Representation for Automatic Tar...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.

关键词： Synthetic aperture radar Target recognition Linear programming Airplanes Remote sensing Support vector machines Minimization

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