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Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification

Seq-Masks: Bridging the gap between appearance and gait mode...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhigang Chang Zhao Yang Yongbiao Chen Qin Zhou Shibao Zheng Institute of Image Processing and Network Engineering Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

Video-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. Gait recognition provides an implicit biometric solution to alleviate the above headache. Nonetheless, it experiences severe performance degeneration as camera view varies. In an attempt to address these problems, in this paper, we propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion. Specifically, to sufficiently validate the effectiveness of our method, we build a novel dataset named MaskMARS based on MARS. Comprehensive experiments on our proposed large wild video Re-ID dataset MaskMARS evidenced our extraordinary performance and generalization capability. Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model. Our codes and dataset MaskMARS will be open-sourced as a strong baseline.

关键词： Measurement Mars image recognition Codes visual communication Surveillance Video sequences

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Learn to overfit better: finding the important parameters for learned image compression

Learn to overfit better: finding the important parameters fo...

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IEEE visual communications and image processing (VCIP)

作者： Honglei Zhang Francesco Cricri Hamed Rezazadegan Tavakoli Maria Santamaria Yat-Hong Lam Miska M. Hannuksela Nokia Technologies Finland

ISBN: (纸本)9781728173221

For most machine learning systems, overfitting is an undesired behavior. However, overfitting a model to a test image or a video at inference time is a favorable and effective technique to improve the coding efficiency of learning-based image and video codecs. At the encoding stage, one or more neural networks that are part of the codec are finetuned using the input image or video to achieve a better coding performance. The encoder en-codes the input content into a content bitstream. If the finetuned neural network is part (also) of the decoder, the encoder signals the weight update of the finetuned model to the decoder along with the content bitstream. At the decoding stage, the decoder first updates its neural network model according to the received weight update, and then proceeds with decoding the content bitstream. Since a neural network contains a large number of parameters, compressing the weight update is critical to reducing bitrate overhead. In this paper, we propose learning-based methods to find the important parameters to be overfitted, in terms of rate-distortion performance. Based on simple distribution models for variables in the weight update, we derive two objective functions. By optimizing the proposed objective functions, the importance scores of the parameters can be calculated and the important parameters can be determined. Our experiments on lossless image compression codec show that the proposed method significantly outperforms a prior-art method where overfitted parameters were selected based on heuristics. Furthermore, our technique improved the compression performance of the state-of-the-art lossless image compression codec by 0.1 bit per pixel.

关键词： Learning systems image coding visual communication Neural networks Bit rate Rate-distortion Machine learning

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Person search by natural language description in Vietnamese using pre-trained visual-textual attributes alignment model 13

Person search by natural language description in Vietnamese ...

引用

13th International conference Knowledge and Systems Engineering, KSE 2021

作者： Thuy Pham, Thi Thanh Nguyen, Van-Thanh Nguyen, Hong-Quan Le, Minh-Quan Phan, Hoai Do, Thi-Ngoc-Diep Nguyen, Thuy-Binh Tran, Thanh-Hai Le, Thi-Lan Academy of People Security Faculty of Information Security Hanoi Viet Nam Hanoi University of Science and Technology School of Electronics and Telecommunications Hanoi Viet Nam Viet-Hung Industrial University Faculty of Information Technology Hanoi Viet Nam Hanoi University of Science and Technology Mica Hanoi Viet Nam University of Transport and Communications Faculty of Electrical and Electronics Engineering Hanoi Viet Nam

ISBN: (纸本)9781665499750

Person search by natural language description is a challenging task as it has to model and learn visual-text semantic embedding. While several works have been dedicated to person search by English descriptions, few attempts have been made for other languages. As a result, it lacks of available resource for person search in these languages. Inspired by transfer learning idea in image classification, in this paper, we propose a method for person search by natural language description in Vietnamese using a model whose weights are trained on a large scale dataset for person search in English. To this end, first, the published network architecture of ViTAA that allows to learn effectively the alignment between visual and textual attributes is employed in this work. Then, we propose to apply different techniques for Vietnamese language processing to analyze and extract relevant elements from descriptions in Vietnamese to feed to the network. Finally, to leverage the information learnt from a large scale dataset, we employ model weights trained from a large scale dataset to a dataset dedicated to person search by natural language description in Vietnamese - VnPersonSearch. The obtained accuracies at Top-1, Top-5 and Top-10 for VnPersonSearch are 61.57%, 83.93% and 90.67% respectively. This means that the proposed method can return relevant persons with very high accuracy in first ten results. © 2021 IEEE.

关键词： Semantics

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Research on image text generation based on word2vec visual vocabulary attention

Research on image text generation based on word2vec visual v...

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communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Danyang Li Yahui Zhao Rongyi Cui Linlin Zhao Intelligent Information Processing Lab. Yanbian University Yanji Jilin China

ISBN: (纸本)9781665447034

A method of image text generation based on the combination of word2vec keyword extraction and attention mechanism is proposed. First, the co-occurring words with visual entities in the description set were extracted for each image in the dataset; Then the similarity was calculated for the extracted keywords, the similar words were filtered out to expand the keyword list, and the words in the vocabulary were retained to create new descriptions for the images. Finally, the test set images were combined with attention mechanism to generate description text. The experiments prove that the method proposed in this paper can achieve automatic annotation of images and can effectively solve the attention diffusion problem in the process of image text generation.

关键词： Computer science Vocabulary visualization Annotations communications technology

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JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for Object Detection and Tracking

JADD-GAN: A Joint Attention Generative Adversarial Data Fusi...

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IEEE International conference on High Performance Computing and communications (HPCC)

作者： Guoxia Xu Hao Wang Meng Zhao Hu Zhu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. The effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem.

关键词： visualization Data integration Object detection Sensor fusion Feature extraction Generators Sensors

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Enhanced Cross Component Sample Adaptive Offset for AVS3

Enhanced Cross Component Sample Adaptive Offset for AVS3

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IEEE visual communications and image processing (VCIP)

作者： Yunrui Jian Jiaqi Zhang Junru Li Suhong Wang Shanshe Wang Siwei Ma Wen Gao Shenzhen Graduate School Peking University Shenzhen China University of Chinese Academy of Science Beijing China Institute of Digital Media Peking University Beijing China

ISBN: (纸本)9781728173221

Cross-component prediction has great potential for removing the redundancy of multi-components. Recently, cross-component sample adaptive offset (CCSAO) was adopted in the third generation of Audio Video coding Standard (AVS3), which utilizes the intensities of co-located luma samples to determine the offsets of chroma sample filters. However, the frame-level based offset is rough for various content, and the edge information of classified samples is ignored. In this paper, we propose an enhanced CCSAO (ECCSAO) method to further improve the coding performance. Firstly, four selectable 1-D directional patterns are added to make the mapping between luma and chroma components more effectively. Secondly, one four-layer quad-tree based structure is designed to improve the filtering flexibility of CCSAO. Experimental results show that the proposed approach achieves 1.51%, 2.33% and 2.68% BD-rate savings for All-Intra (AI), Random-Access (RA) and Low Delay B (LD) configurations compared to AVS3 reference software, respectively. A subset improvement of ECCSAO has been adopted by AVS3.

关键词： image coding visual communication Simulation Urban areas Redundancy Information filters Software

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Probability-based decoder-side intra mode derivation for VVC

Probability-based decoder-side intra mode derivation for VVC

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IEEE visual communications and image processing (VCIP)

作者： Yang Wang Li Zhang Kai Zhang Yuwen He Hongbin Liu Multimedia Lab Bytedance Inc.

ISBN: (纸本)9781728173221

Intra prediction is typically used to exploit the spatial redundancy in video coding. In the latest video coding standard Versatile Video Coding (VVC), 67 intra prediction modes are adopted in intra prediction. The encoder selects the best one from 67 modes and signals it to the decoder. Bits consuming of signaling the selected mode may limit the coding efficiency. To reduce the overhead of signaling the intra prediction mode, a probability-based decoder-side intra mode derivation (P-DIMD) is proposed in this paper. Specifically, an intra prediction mode candidate set is constructed based on the probabilities of intra prediction modes. The probability of an intra prediction mode is mainly estimated in two ways. First, the textures are typically continuous within a local region and intra prediction modes of neighboring blocks are similar to each other. Second, some intra prediction modes are preferable to be used than others. For each intra prediction mode in the constructed candidate set, intra prediction is processed on a template to calculate a cost. The intra prediction mode with the minimum cost is determined as the optimal mode and used in the intra prediction of the current block. Experimental results demonstrate that P-DIMD can achieve 0.56% BD-rate saving on average compared to VTM-11.0 under all intra configuration.

关键词： Video coding Costs image coding visual communication Redundancy Decoding Artificial intelligence

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Kalman filter-based prediction refinement and quality enhancement for geometry-based point cloud compression

Kalman filter-based prediction refinement and quality enhanc...

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IEEE visual communications and image processing (VCIP)

作者： Lu Wang Jian Sun Hui Yuan Raouf Hamzaoui Xiaohui Wang School of Info. Sci. and Eng. Shandong University Qingdao China School of Contr. Sci. and Eng. Shandong University Jinan China School of Eng. and Sustain. Dev. De Montfort University Leicester UK

ISBN: (纸本)9781728173221

A point cloud is a set of points representing a three-dimensional (3D) object or scene. To compress a point cloud, the Motion Picture Experts Group (MPEG) geometry-based point cloud compression (G-PCC) scheme may use three attribute coding methods: region adaptive hierarchical transform (RAHT), predicting transform (PT), and lifting transform (LT). To improve the coding efficiency of PT, we propose to use a Kalman filter to refine the predicted attribute values. We also apply a Kalman filter to improve the quality of the reconstructed attribute values at the decoder side. Experimental results show that the combination of the two proposed methods can achieve an average Bjøntegaard delta bitrate of −0.48%, −5.18%, and −6.27% for the Luma, Chroma Cb, and Chroma Cr components, respectively, compared with a recent G-PCC reference software.

关键词： Point cloud compression image coding Three-dimensional displays visual communication Transform coding Transforms Motion pictures

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DVC-P: Deep Video Compression with Perceptual Optimizations

DVC-P: Deep Video Compression with Perceptual Optimizations

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IEEE visual communications and image processing (VCIP)

作者： Saiping Zhang Marta Mrak Luis Herranz Marc Górriz Blanch Shuai Wan Fuzheng Yang State Key Laboratory of Integrated Services Networks Xidian University Xi'an China BBC Research & Development The Lighthouse White City Place London UK Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728173221

Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual op-timizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD- rate equivalent, on average.

关键词： Interpolation visualization image coding visual communication Bit rate Rate distortion theory Video compression

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Weighted Robinson Compass Gradient and Charbonnier Penalty Function as a Loss Function

Weighted Robinson Compass Gradient and Charbonnier Penalty F...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Rachna U Dhruv Shindhe S S N Omkar Department of Electronics and Communications PES University Bangalore India ARTPARK Indian Institute Of Science Bangalore India Department of Aerospace Indian Institute Of Science Bangalore India

Loss function quantifies how well a machine learning model is performing, it computes the difference between the ground truth and the reconstructed outcomes. In image processing tasks such as image dehazing, image deblurring and image denoising the most popular loss functions used are Mean Squared error(MSE) and perceptual loss. MSE is less computationally expensive but fails to recreate precise boundaries whereas perceptual loss is more computationally expensive as they use feature vectors from a pre-trained model like VGG-net. In smaller on the edge systems like autonomous navigation systems, one of the most critical tasks is relying on visual cues, the take in the video feed from the environment and make decision on it after a few prepossessing steps to eliminate things like physical barriers, blurr, and smoke that clutter a robot’s environment are one of the several factors that affect autonomous navigation. We need systems that aren’t computationally expensive but also have a good reconstruction. For this we propose a loss function that incorporates gradients along with MSE for faster and better reconstruction.

关键词： Training visualization Technological innovation Computational modeling Mean square error methods image restoration Task analysis

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