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Vehicle re-identification with location and time stamps 32

Vehicle re-identification with location and time stamps

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Lv, Kai Du, Heming Hou, Yunzhong Deng, Weijian Sheng, Hao Jiao, Jianbin Zheng, Liang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University University of Chinese Academy of Sciences Australian National University

ISBN: (纸本)9781728125060

This paper focuses on the problem of vehicle re-identification (Re-ID). In our attempt, we propose a re-identification framework by exploiting vehicle location and time stamps. The location and time information have the potential to cover the shortage of appearance-based feature representations. First, we introduce an ensemble technique to combine the informative cues of multiple Re-ID models effectively. To further improve the accuracy, we then build up a system to acquire the vehicle location and time stamps. Specifically, we utilize the detected results to obtain the needed information. With the help of the proposed system, we can remove irrelevant images from a given ranking list. Our system finished 3rd place in the 2019 AI-City challenge for city-scale multi-camera vehicle re-identification. © 2019 ieee computer Society. All rights reserved.

关键词： Vehicles

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LSQ+: Improving low-bit quantization through learnable offsets and better initialization

LSQ+: Improving low-bit quantization through learnable offse...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yash Bhalgat Jinwon Lee Markus Nagel Tijmen Blankevoort Nojun Kwak Qualcomm AI Research Qualcomm Technologies Inc. Qualcomm AI Research Qualcomm Technologies Netherlands B.V. Seoul National University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, with skewed positive and negative ranges. Typical learnable quantization schemes [5], [7] assume unsigned quantization for activations and quantize all negative activations to zero which leads to significant loss in performance. Naively using signed quantization to accommodate these negative values requires an extra sign bit which is expensive for low-bit (2-, 3-, 4-bit) quantization. To solve this problem, we propose LSQ+, a natural extension of LSQ [7], wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations. Gradient-based learnable quantization schemes also commonly suffer from high instability or variance in the final training performance, hence requiring a great deal of hyper-parameter tuning to reach a satisfactory performance. LSQ+ alleviates this problem by using an MSE-based initialization scheme for the quantization parameters. We show that this initialization leads to significantly lower variance in final performance across multiple training runs. Overall, LSQ+ shows state-of-the-art results for EfficientNet and MixNet and also significantly outperforms LSQ for low-bit quantization of neural nets with Swish activations (e.g.: 1.8% gain with W4A4 quantization and upto 5.6% gain with W2A2 quantization of EfficientNet-B0 on ImageNet dataset). To the best of our knowledge, ours is the first work to quantize such architectures to extremely low bit-widths.

关键词： Quantization (signal) Training Clamps Neural networks Artificial intelligence computer architecture Minimization

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Global sum pooling: A generalization trick for object counting with small datasets of large images 32

Global sum pooling: A generalization trick for object counti...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Aich, Shubhra Stavness, Ian Department of Computer Science University of Saskatchewan Canada

ISBN: (纸本)9781728125060

In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. We illustrate that conventional global average pooling (GAP) based models are unreliable due to the patchwise cancellation of true overestimates and underestimates for patchwise inference. To overcome this limitation and reduce overfitting caused by the training on full-resolution images, we propose to employ global sum pooling (GSP) instead of GAP or fully connected (FC) layers at the backend of a convolutional network. Although computationally equivalent to GAP, we show through comprehensive experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. This generalization capability allows GSP to avoid both patchwise cancellation and overfitting by training on small patches and inference on full-resolution images as a whole. We evaluate our approach on four different aerial image datasets – two car counting datasets (CARPK and COWC), one crowd counting dataset (ShanghaiTech;parts A and B) and one new challenging dataset for wheat spike counting. Our GSP models improve upon the state-of-the-art approaches on all four datasets with a simple architecture. Also, GSP architectures trained with smaller-sized image patches exhibit better localization property due to their focus on learning from smaller regions while training. © 2019 ieee computer Society. All rights reserved.

关键词： Antennas

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NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results 32

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cai, Jianrui Gu, Shuhang Timofte, Radu Zhang, Lei Liu, Xiao Ding, Yukang He, Dongliang Li, Chao Fu, Yi Wen, Shilei Feng, Ruicheng Gu, Jinjin Qiao, Yu Dong, Chao Park, Dongwon Chun, Se Young Yoon, Sanghoon Kwak, Junhyung Son, Donghee Zamir, Syed Waqas Arora, Aditya Khan, Salman Khan, Fahad Shahbaz Shao, Ling Wei, Zhengping Liu, Lei Cai, Hong Li, Darui Gao, Fujie Hui, Zheng Wang, Xiumei Gao, Xinbo Cheng, Guoan Matsune, Ai Li, Qiuyu Zhu, Leilei Zang, Huaijuan Zhan, Shu Qiu, Yajun Wang, Ruxin Li, Jiawei Jing, Yongcheng Song, Mingli Liu, Pengju Zhang, Kai Liu, Jingdong Liu, Jiye Zhang, Hongzhi Zuo, Wangmeng Tang, Wenyi Liu, Jing Kim, Youngjung Shin, Changyeop Kim, Minbeom Kim, Sungho Michelini, Pablo Navarrete Liu, Hanwen Zhu, Dan Xu, Xuan Li, Xin Bai, Furui Sun, Xiaopeng Zha, Lin Huang, Yuanfei Lu, Wen Cao, Yanpeng Chen, Du He, Zewei Sun Anshun Tang, Siliang Fan, Hongfei Li, Xiang Li, Guo Zhang, Wenjie Zhang, Yumei He, Qingwen Qin, Jinghui Huang, Lishan Shi, Yukai Wei, Pengxu Wen, Wushao Lin, Liang Yu, Jun Xie, Guochen Li, Mengyan Chen, Rong Luo, Xiaotong Hong, Chen Qu, Yanyun Li, Cuihua Liu, Zhi-Song Wang, Li-Wen Li, Chu-Tak Zhao, Can Li, Bowen Tsai, Chung-Chi Chuang, Shang-Chih Choi, Joon-Hee Kim, Joonsoo Jiang, Xiaoyun Pan, Ze Lv, Qunbo Tan, Zheng He, Peidong Hong Kong Polytech Univ Hong Kong Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Baidu Inc Comp Vis Technol VIS Dept Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Ulsan Natl Inst Sci & Technol UNIST Ulsan South Korea Kakao Jeju Si South Korea CASTIS Multimedia R&D Ctr Seoul South Korea NALBI Inc Seoul South Korea Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates LiveMe Inc West Hollywood CA USA Xidian Univ Sch Elect Engn Xian Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Yunnan Univ Kunming Yunnan Peoples R China Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Harbin Inst Technol Sch Comp Sci & Technol 92 West Da Zhi St Harbin Peoples R China Harbin Ultravis Technol 6 Fuhua St Harbin Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China Agcy Def Dev Seoul South Korea BOE Technol Grp Co Ltd Beijing Peoples R China West Virginia Univ Morgantown WV 26506 USA Xidian Univ Xian Peoples R China Kiwi Image Technol Co Ltd Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Zhejiang Peoples R China Beijing Kingsoft Cloud Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Sci & Technol China Beijing Peoples R China XiDian Univ Kiwi Image Technol Co Ltd Xian Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9781728125060

This paper reviewed the 3rd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we introduced a novel real-world super-resolution dataset (RealSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution.

关键词： computer vision

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On Visible Adversarial Perturbations & Digital Watermarking 31

On Visible Adversarial Perturbations & Digital Watermarking

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hayes, Jamie UCL London England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.

关键词： Perturbation methods Watermarking Computational modeling Visualization Task analysis Image restoration computer vision

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Multi-camera tracking of vehicles based on deep features re-id and trajectory-based camera link models 32

Multi-camera tracking of vehicles based on deep features re-...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Hsu, Hung-Min Huang, Tsung-Wei Wang, Gaoang Cai, Jiarui Lei, Zhichao Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Due to the exponential growth of traffic camera net- works, the need for multi-camera tracking (MCT) for in- telligent transportation has received more and more at- tention. The challenges of MCT include similar vehicle models, significant feature variation in different orienta- tions, color variation of the same car due to lighting con- ditions, small object sizes and frequent occlusion, as well as the varied resolutions of videos. In this work, we pro- pose an MCT system, which combines single-camera track- ing (SCT) and inter-camera tracking (ICT) which includes trajectory-based camera link model and deep feature re- *** SCT, we use a TrackletNet Tracker (TNT), which effectively generates the moving trajectories of all de- tected vehicles by exploiting temporal and appearance in- formation of multiple tracklets that are created by associat- ing bounding boxes of detected vehicles. The tracklets are generated based on CNN feature matching and intersection- over-union (IOU) in every single-camera view. In terms of deep feature re-identification, we exploit the temporal at- tention model to extract the most discriminant feature of each trajectory. In addition, we propose the trajectory- based camera link models with order constraint to effi- ciently leverage the spatial and temporal information for ICT. The proposed method is evaluated on CVPR AI City Challenge2019 City Flow dataset, achieving IDF1 70.59%, which outperforms competing methods. © 2019 ieee computer Society. All rights reserved.

关键词： Vehicles

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Fine-grained Activity recognition in Baseball Videos 31

Fine-grained Activity Recognition in Baseball Videos

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Piergiovanni, A. J. Ryoo, Michael S. Indiana Univ Dept Comp Sci Bloomington IN 47408 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.

关键词： Videos Activity recognition Sports Task analysis Cameras computer vision Games

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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Driving scene-retrieval by example from large-scale data 32

Driving scene-retrieval by example from large-scale data

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Hornauer, Sascha Yellapragada, Baladitya Ranjbar, Arian Yu, Stella X. International Computer Science Institute University of California Berkeley United States PATH University of California Berkeley United States

ISBN: (纸本)9781728125060

Many machine learning approaches train networks with input from large datasets to reach high task performance. Collected datasets, such as Berkeley Deep Drive Video (BDD-V) for autonomous driving, contain a large variety of scenes and hence features. However, depending on the task, subsets, containing certain features more densely, support training better than others. For example, training networks on tasks such as image segmentation, bounding box detection or tracking requires an ample amount of objects in the input data. When training a network to perform optical flow estimation from first-person video, over-proportionally many straight driving scenes in the training data may lower generalization to turns. Even though some scenes of the BDD-V dataset are labeled with scene, weather or time of day information, these may be too coarse to filter the dataset best for a particular training task. Furthermore, even defining an exhaustive list of good label-types is complicated as it requires choosing the most relevant concepts of the natural world for a task. Alternatively, we investigate how to use examples of desired data to retrieve more similar data from a large-scale dataset. Following the paradigm of"I know it when I see it", we present a deep learning approach to use driving examples for retrieving similar scenes from the BDD-V dataset. Our method leverages only automatically collected labels. We show how we can reliably vary time of the day or objects in our query examples and retrieve nearest neighbors from the dataset. Using this method, already collected data can be filtered to remove bias from a dataset, removing scenes regarded too redundant to train on. © 2019 ieee computer Society. All rights reserved.

关键词： Large dataset

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Preselection based subjective preference evaluation for the quality of underwater images 32

Preselection based subjective preference evaluation for the ...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Yang, Miao Du, Yixiang Huang, Yue Liu, Hantao Wei, Zhiqiang Hu, Jintong Hu, Ke Sheng, Zhibin School of Electronic Engineering HuaiHai Institute of Technology China Marine Equipment and Technology Institute Jiangsu University of Science and Technology China Qingdao National Laboratory of Marine Science and Technology China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Ministry of Education China School of Computer Science and Informatics Cardiff University United Kingdom Ocean University of China School of Information Engineering Mining and Technology University of China School of Information and Control Engineering

ISBN: (纸本)9781728125060

Underwater images contain an interactive mixture of distortions due to the physicochemical property of water and the instability of imaging systems, which differ from those in natural images. We cannot obtain the pristine underwater image as the reference applied in the traditional benchmark databases, and the groups of gradual distortions either. In this paper, a novel preselection based preference label evaluation method is proposed to construct a combined subjective test procedure for an extended preference judgment dataset of underwater images. To the best of our knowledge, this is the first subjective evaluation procedure for underwater images, and also a solution for an expanding visual preference benchmark database. We demonstrate the excellent correlation of the proposed subjective evaluation with the traditional image quality assessment. It is also proven that the proposed subjective evaluation procedure could reflect the slight change of image quality and the authentic quality of a picture more accurately better than the traditional methods. © 2019 ieee computer Society. All rights reserved.

关键词： Database systems

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