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AFRIFASHION1600: A Contemporary African Fashion Dataset for computer vision

AFRIFASHION1600: A Contemporary African Fashion Dataset for ...

引用

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

作者： Oyewusi, Wuraola Fisayo Adekanmbi, Olubayo Ibejih, Sharon Osakuade, Opeyemi Okoh, Ifeoma Salami, Mary Data Sci Nigeria Lagos Nigeria

ISBN: (纸本)9781665448994

This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computer vision ***1600 dataset is available here.

关键词： computer vision conferences Computational modeling Machine learning pattern recognition Task analysis

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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Robust Three-view Triangulation Done Fast

Robust Three-view Triangulation Done Fast

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Hedborg, Johan Robinson, Andreas Felsberg, Michael Linkoping Univ S-58183 Linkoping Sweden

ISBN: (纸本)9781479943098

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L-2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

关键词： Nonlinear optimization Structure from motion Three-view Triangulation Raspberries Solvers Fasting ground truth image data computer vision Systems Personal computers triangulation Gaussian noise

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

引用

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features

Understanding the Nature of First-Person Videos: Characteriz...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Tan, Cheston Goh, Hanlin Chandrasekhar, Vijay Li, Liyuan Lim, Joo-Hwee Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781479943098

First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or "egocentric") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPVversus-TPV classification achieved good baseline performance. Accuracy peaked at 81% for 2-minute clips, but 67% accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data.

关键词： LOW LEVEL Videotapes Pepper golden mosaic virus Dataset images computer vision clipping Clip Camera device

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An Embedded Solution to Visual Mapping for Consumer Drones

An Embedded Solution to Visual Mapping for Consumer Drones

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27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Guyue Li, Zexiang Liu, Ang Yang, Kang Wang, Tao Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China Dafrang Innovat Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781479943098

In this paper, we propose a real-time visual mapping scheme which can be implemented on a low-cost embedded system for consumer-level ratio control (RC) drones. In our work, a 3-dimensional occupancy grid map is obtained based on an estimated trajectory from data fusion of multiple on-board sensors, composed of two downward-facing cameras, two forward-facing cameras, a GPS receiver, a magnetic compass and an inertial measurement unit (IMU) with 3-axis accelerometers and gyroscopes. Taking the advantages of the low-cost FPGA and ARM NEON intrinsics, we run our visual odometry and mapping algorithms at 10Hz on board. Meanwhile, we also present a hierarchical multi-sensor fusion algorithm to provide a robust trajectory for mapping usage. Finally, we verify the feasibility of our approaches and serval potential applications with experimental results in complex indoor/outdoor environments.

关键词： embedded vision sensor fusion visual mapping Sensor fusion MAPPING(MATHEMATICAL) drone honey bees Gyroscopes GPS Receiver Visualization Data fusion Cameras

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A Train Station Surveillance System: Challenges and Solutions

A Train Station Surveillance System: Challenges and Solution...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Ozer, Burak Wolf, Marilyn Perkasie United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781479943098

In this paper, we describe our multi-year development effort to create a smart-camera surveillance system for use in train stations. We address the problem of activity analysis in very crowded areas and offer solutions to the detection/tracking problems in one of the most crowded environments. The smart camera system is installed in several train stations around Tokyo, Japan and is currently deployed analyzing crowd movement and individual gestures of the passengers in the stations. The paper summarizes the technical problems we faced during our research /development stages as well as during system deployment. The hierarchical smart camera system, which can detect the overall crowd movement in the higher level and individual activities in the lower level, is developed by Verificon Corp., a Princeton University startup company. We developed smart camera algorithms and spent several years commercializing the technology in a joint project with Yokogawa Electric. A railway company was the lead customer on this project. Verificon system passed several rounds of testing, running for several months on live data in several Tokyo train stations and was deployed by the railway company. This project has led to several extensions of the original technology.

关键词： surveillance tracking gesture recognitionin

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Streetscore - Predicting the Perceived Safety of One Million Streetscapes

Streetscore - Predicting the Perceived Safety of One Million...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Naik, Nikhil Philipoom, Jade Raskar, Ramesh Hidalgo, Cesar MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781479943098

Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring similar to 1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception.

关键词： internet vision trueskill urban computing urban planning visual analysis beyond semantics

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A Sequential Classifier for Hand Detection in the Framework of Egocentric vision

A Sequential Classifier for Hand Detection in the Framework ...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Betancourt, Alejandro Lopez, Miriam M. Regazzoni, Carlo S. Rauterberg, Matthias Univ Genoa Dept Naval Elect Elect & Telecommun Engn I-16126 Genoa Italy Eindhoven Univ Technol Dept Ind Design Designed Intelligence Grp Eindhoven Netherlands

ISBN: (纸本)9781479943098

Hand detection is one of the most explored areas in Egocentric vision Video Analysis for wearable devices. Current methods are focused on pixel-by-pixel hand segmentation, with the implicit assumption of hand presence in almost all activities. However, this assumption is false in many applications for wearable cameras. Ignoring this fact could affect the whole performance of the device since hand measurements are usually the starting point for higher level inference, or could lead to inefficient use of computational resources and battery power. In this paper we propose a two-level sequential classifier, in which the first level, a hand-detector, deals with the possible presence of hands from a global perspective, and the second level, a hand-segmentator, delineates the hand regions at pixel level in the cases indicated by the first block. The performance of the sequential classifier is stated in probabilistic notation as a combination of both, classifiers allowing to test new hand-detectors independently of the type of segmentation and the dataset used in the training stage. Experimental results show a considerable improvement in the detection of true negatives, without compromising the performance of the true positives.

关键词： Hand Detection First Person vision Egocentric vision Video Processing

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