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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (cvpr) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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A Geometric ConvNet on 3D Shape Manifold for Gait recognition

A Geometric ConvNet on 3D Shape Manifold for Gait Recognitio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hosni, Nadia Ben Amor, Boulbaba Univ Manouba CRISTAL Manouba Tunisia Univ Lille CNRS 9189 IMT Lille Douai CRIStAL Lille France Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728193601

In this work we propose a geometric deep convolutional auto-encoder (DCAE) for the purpose of gait recognition by analyzing time-varying 3D skeletal data. Sequences are viewed as time-parameterized trajectories on the Kendall shape space S, results of modding out shape-preserving transformations (scaling, translations and rotations). The accommodation of ConvNet architectures to properly approximate manifold-valued trajectories on the underlying non-linear space S is a must. Thus, we make use of geometric steps prior to the encoding-decoding scheme. That is, shape trajectories are first log-mapped to tangent spaces attached to the shape space at a time-varying average trajectory mu, then, obtained vectors are transported to a common tangent space T mu((0)) (S) at the starting point of mu. Without applying any prior temporal alignment (e.g. Dynamic Time Warping) or modeling (e.g. HMM, RNN), the transported trajectories are then fed to a convolutional auto-encoder to build subject-specific latent spaces. The proposed approach was tested on two publicly available datasets. Our approach outperforms existing approaches on CMU gait dataset, while performances on UPCV K2 are comparable to existing approaches. We demonstrate that combining geometric invariance (i.e. Kendall's representation) with our data-driven ConvNet model is suitable to alleviate spatial and temporal variability, respectively.

关键词： Trajectories

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AI City Challenge 2020-computer vision for Smart Transportation Applications

AI City Challenge 2020-Computer Vision for Smart Transportat...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chang, Ming-Ching Chiang, Chen-Kuo Tsai, Chun-Ming Chang, Yun-Kai Chiang, Hsuan-Lun Wang, Yu-An Chang, Shih-Ya Li, Yun-Lun Tsai, Ming-Shuin Tseng, Hung-Yu SUNY Albany Albany NY 12222 USA Natl Chung Cheng Univ Minxiong Taiwan Univ Taipei Taipei Taiwan

ISBN: (纸本)9781728193601

We present methods developed in our participation of the AI City 2020 Challenge (AIC20) and report evaluation results in this contest. With the blooming of AI computer vision techniques, vehicle detection, tracking, identification, and counting all have advanced significantly. However, whether these technologies are ready for real-world smart transportation usage is still a open question. The goal of this work is to apply and integrate state-of-the-art techniques for solving the challenge problems under a standardized setup and evaluation. We participated all 4 AIC20 challenge tracks (T1 to T4). In T1 challenge, we perform vehicle counting by associating deep features extracted from Mask-RCNN detections and tracklets, followed by vehicle movement zone matching. In T2 challenge, we perform vehicle type and color classification and then rank matching vehicles using a PGAM re-id network. In T3 challenge, we proposed a new Multi-Camera Tracking Network (MTCN) that takes single-camera vehicle tracking as input, and performs multi-camera tracklet fusion and linking, by jointly optimizing the matching of vehicle appearance and physical features. In T4 challenge, we adopt a leading method based on perspective detection and spatial-temporal matrix discriminating, and improve it with background modeling for traffic anomaly detection. We achieved top-6 and top-4 performance for T3 and T4 challenges respectively in the AIC20 general leaderboard.

关键词： computer vision

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PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

PI-Net: A Deep Learning Approach to Extract Topological Pers...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Som, Anirudh Choi, Hongjun Ramamurthy, Karthikeyan Natesan Buman, Matthew P. Turaga, Pavan Arizona State Univ Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA IBM Res Albany NY USA Arizona State Univ Coll Hlth Solut Tempe AZ 85287 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://***/anirudhsom/PI-Net.

关键词： Machine learning computer architecture computer vision Task analysis Topology Feature extraction Data models

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Trident Dehazing Network

Trident Dehazing Network

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jing Wu, Haiyan Xie, Yuan Qu, Yanyun Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9781728193601

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy-free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature;the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder;and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： computer vision

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Dynamic Inference: A New Approach Toward Efficient Video Action recognition

Dynamic Inference: A New Approach Toward Efficient Video Act...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Wenhao He, Dongliang Tan, Xiao Chen, Shifeng Yang, Yi Wen, Shilei Chinese Acad Sci Shenzhen Inst Adv Technol MMLab Beijing Peoples R China Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Technol Sydney Sydney NSW Australia Baidu Beijing Peoples R China

ISBN: (纸本)9781728193601

Though action recognition in videos has achieved great success recently, it remains a challenging task due to the massive computational cost. Designing lightweight networks is a possible solution, but it may degrade the recognition performance. In this paper, we innovatively propose a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos. The dynamic inference approach can be achieved from aspects of the network depth and the number of input video frames, or even in a joint input-wise and network depth-wise manner. In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module. The inference is carried out progressively on the grid by following some predefined route, whenever the inference process comes across a checkpoint, an early prediction can be made depending on whether the early stop criteria meets. For the proof-of-concept purpose, we instantiate several dynamic inference frameworks. In these instances, we overcome the drawback of limited temporal coverage resulted from an early prediction by a novel frame permutation scheme, and alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module. Extensive experiments are conducted to thoroughly analyze the effectiveness of our ideas and to inspire future research efforts. Results on various datasets also evident the superiority of our approach.

关键词： Forecasting

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Learning Sparse Neural Networks Through Mixture-Distributed Regularization

Learning Sparse Neural Networks Through Mixture-Distributed ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Chang-Ti Chen, Jun-Cheng Wu, Ja-Ling Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

L-0-norm regularization is one of the most efficient approaches to learn a sparse neural network. Due to its discrete nature, differentiable and approximate regularizations based on the concrete distribution [31] or its variants are proposed as alternatives;however, the concrete relaxation suffers from high-variance gradient estimates and is limited to its own concrete distribution. To address these issues, in this paper, we propose a more general framework for relaxing binary gates through mixture distributions. With the proposed method, any mixture pair of distributions converging to delta(0) and delta(1) can be applied to construct smoothed binary gates. We further introduce a reparameterization method for the smoothed binary gates drawn from mixture distributions to enable efficient gradient gradient-based optimization under the proposed deep learning algorithm. Extensive experiments are conducted, and the results show that the proposed approach achieves better performance in terms of pruned architectures, structured sparsity and the reduced number of floating point operations (FLOPs) as compared with other state-of-the-art sparsity-inducing methods.

关键词： Logic gates Training Neurons computer architecture Artificial neural networks Computational modeling Exponential distribution

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Thermal Image Super-Resolution Challenge - PBVS 2020

Thermal Image Super-Resolution Challenge - PBVS 2020

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Guo, Lin Hou, Jiankun Mehri, Armin Behjati Ardakani, Parichehr Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Upla, Kishor P. Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Almasri, Feras Debeir, Olivier Nathan, Sabari Kansal, Priya Gutierrez, Nolan Mojra, Bardia Beksi, William J. ESPOL Escuela Super Politecn Litoral CIDIS Fac Ingn Elect & Comp 30-5 Via PerimetralPOB 09-01-5863 Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Oklahoma State Univ Stillwater OK 74078 USA SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Couger Inc Tokyo Japan Univ Texas Arlington Dept Comp Sci & Engn Robot Vis Lab Arlington TX 76019 USA

ISBN: (纸本)9781728193601

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.

关键词： computer vision

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FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object recognition

FoNet: A Memory-efficient Fourier-based Orthogonal Network f...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wei, Feng Uyen Trang Nguyen Jiang, Hui York Univ Dept Elect Engn & Comp Sci 4700 Keele St Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781728193601

The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with the increasing depth of the network, which is a major constraint for efficient network training and inference on modern GPUs with limited memory. Several studies show that the feature maps (as generated after the convolutional layers) are the big bottleneck in this memory problem. Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain. In this paper, we propose a Fourier-based Orthogonal Network (FoNet) that incorporates orthogonal representations and performs both the convolution and the activation operations in the spectral domain to achieve memory reduction. The performance of our FoNet is evaluated on four standard object recognition benchmarks (i.e., MNIST, CIFAR-10, SVHN, and ImageNet), and compared with four state-of-the-art implementations (i.e., LeNet, AlexNet, VGG, and DenseNet). Encouragingly, FoNet is able to reduce memory consumption by about 60% without significant loss of performance for all tested network architectures.

关键词： Convolution

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NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution:...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P. S. Puthussery, Densen Jiji, C., V Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland OPPO Res Dongguan Guangdong Peoples R China Yonsei Univ Seoul South Korea Facebook Menlo Pk CA USA Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China Xidian Univ Xian Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China SI Analyt Co Ltd 441 Expo Ro Daejeon 34051 South Korea Toyota Technol Inst TTI Toyota Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Founder Grp State Key Lab Digital Publishing Technol Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Microsoft Res Beijing Peoples R China Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Amazon Web Serv Seattle WA USA Univ Illinois Champaign IL USA Korea Univ Seoul South Korea Wix Com Ltd Tel Aviv Israel Guangdong Univ Technol Guangzhou Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Dalian Maritime Univ Dalian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

关键词： computer vision

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