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Learning Fashion By Simulated Human Supervision 31

Learning Fashion By Simulated Human Supervision

引用

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

作者： Alshan, Eli Alpert, Sharon Neuberger, Assaf Bubis, Nathaniel Oks, Eduard Amazon Lab126 Sunnyvale CA 94089 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We consider the task of predicting subjective fashion traits from images using neural networks. Specifically, we are interested in training a network for ranking outfits according to how well they fit the user. In order to capture the variability induced by human subjective considerations, each training example is annotated by a panel of fashion experts. Similarly to previous works on subjective data, the panel votes are converted to a classification or regression problem and the corresponding network is trained and evaluated using standard objective metrics. The question is which objective metric, if any, is most suitable to measure the performance of a network trained for subjective tasks? In this paper, we conducted human approval tests for outfit ranking networks trained using various objective metrics. We show that these metrics do not adequately estimate the human approval of subjective tasks. Instead, we introduce a supervising network that unlike objective metrics, is designed to capture the variability induced by human subjectivity. We use it to supervise our outfit ranking network and we demonstrate empirically, that training our outfit ranking network with the suggested supervising network achieves greater approval ratings from human subjects.

关键词： Task analysis Training Measurement Level set Prediction algorithms computer vision Standards

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Convolutional Neural Networks for Iris Presentation Attack Detection: Toward Cross-Dataset and Cross-Sensor Generalization 31

Convolutional Neural Networks for Iris Presentation Attack D...

引用

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

作者： Hoffman, Steven Sharma, Renu Ross, Arun Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Iris recognition systems are vulnerable to presentation attacks where an adversary employs artifacts such as 2D prints of the eye, plastic eyes, and cosmetic contact lenses to obfuscate their own identity or to spoof the identity of another subject. In this work, we design a Convolutional Neural Network (CNN) architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability. The salient features of the proposed approach include: (a) the use of the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss;(b) the tessellation of the iris region into overlapping patches to enable data augmentation as well as to learn features that are location agnostic;(c) fusion of information across patches to enhance detection accuracy;(d) incorporating a "segmentation mask" in order to automatically learn the relative importance of the pupil and iris regions;(e) generation of a "heat map" that displays patch-wise presentation attack information, thereby accounting for artifacts that may impact only a small portion of the iris region. Experiments confirm the efficacy of the proposed approach.

关键词： Iris recognition Iris Image segmentation Plastics Sensors Lenses Hardware

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Deep Residual Network with Enhanced Upscaling Module for Super-Resolution 31

Deep Residual Network with Enhanced Upscaling Module for Sup...

引用

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

作者： Kim, Jun-Hyuk Lee, Jong-Seok Yonsei Univ Sch Integrated Technol Seoul South Korea

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Single image super-resolution (SR) have recently shown great performance thanks to the advances in deep learning. In the middle of the deep networks for SR, a part that increases image resolution is required, for which a sub-pixel convolution layer is known as an efficient way. However, we argue that the method has room for improvement, and propose an enhanced upscaling module (EUM), which achieves improvement by utilizing nonlinear operations and skip connections. Employing our proposed EUM, we propose a novel deep residual network for SR, called EUSR. Our proposed EUSR was ranked in the 9th place among 24 teams in terms of SSIM in track 1 of the NTIRE 2018 SR Challenge [25]. In addition, we experimentally show that EUSR has comparable performance on x2 and x4 SR for four benchmark datasets to the state-of-the-art methods, and outperforms them on a large scaling factor (x8).

关键词： Convolution Image resolution Feature extraction Training Image reconstruction computer vision Signal resolution

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Onboard Stereo vision for Drone Pursuit or Sense and Avoid 31

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

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

作者： Cigla, Cevahir Thakker, Rohan Matthies, Larry Aselsan Inc Yenimahalle Ankara Turkey CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We describe a new, on-board, short range perception system that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

关键词： Radar tracking Target tracking Drones Cameras Three-dimensional displays Observers

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Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling 31

Part-based Player Identification using Deep Convolutional Re...

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

作者： Senocak, Arda Oh, Tae-Hyun Kim, Junsik Kweon, In So Korea Adv Inst Sci & Technol Dept EE Daejeon South Korea MIT CSAIL Cambridge MA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.

关键词： Videos Cameras Task analysis Visualization Training Sports Face recognition

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Cosmetic Features Extraction by a Single Image Makeup Decomposition 31

Cosmetic Features Extraction by a Single Image Makeup Decomp...

引用

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

作者： Yamagishi, Kanami Yamamoto, Shintaro Kato, Takuya Morishima, Shigeo Waseda Univ Tokyo Japan Waseda Res Inst Sci & Engn Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, a large number of makeup images have been shared on social media. Most of these images lack information about the cosmetics used, such as color, glitter or etc., while they are difficult to infer due to the diversity of skin color or lighting conditions. In this paper, our goal is to estimate cosmetic features only from a single makeup image. Previous work has measured the material parameters of cosmetic products from pairs of images showing the face with and without makeup, but such comparison images are not always available. Furthermore, this method cannot represent local effects such as pearl or glitter since they adapted physically-based reflectance models. We propose a novel image-based method to extract cosmetic features considering both color and local effects by decomposing the target image into makeup and skin color using Difference of Gaussian (DoG). Our method can be applied for single, standalone makeup images, and considers both local effects and color. In addition, our method is robust to the skin color difference due to the decomposition separating makeup from skin. The experimental results demonstrate that our method is more robust to skin color difference and captures characteristics of each cosmetic product.

关键词： Image color analysis Feature extraction Dogs Skin Lighting computer vision pattern recognition

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GenLR-Net: Deep framework for very low resolution face and object recognition with generalization to unseen categories 31

GenLR-Net: Deep framework for very low resolution face and o...

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

作者： Mudunuri, Sivaram Prasad Sanyal, Soubhik Biswas, Soma Indian Inst Sci Dept Elect Engn Bangalore Karnataka India Max Planck Inst Intelligent Syst Perceiving Syst Dept Tubingen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Matching very low resolution images of faces and objects with high resolution images in the database has important applications in surveillance scenarios, street-to-shop matching for general objects, etc. Matching across huge resolution difference along with variations in illumination, view-point, etc. makes the problem quite challenging. The problem becomes even more difficult if the testing objects have not been seen during training. In this work, we propose a novel deep convolutional neural network architecture to address these problems. We systematically introduce different kinds of constraints at different stages of the architecture so that the approach can recognize low resolution images as well as generalize well to images of unseen categories. The reason behind each additional step along with its effect on the overall performance is thoroughly analyzed. Extensive experiments are conducted on two face and object datasets which justifies the effectiveness of the proposed approach for handling these real-life challenging scenarios.

关键词： Image resolution Face Training Object recognition Databases Testing Task analysis

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

引用

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

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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Automatic Cricket Highlight generation using Event-Driven and Excitement-Based features 31

Automatic Cricket Highlight generation using Event-Driven an...

引用

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

作者： Shukla, Pushkar Sadana, Hemant Bansal, Apaar Verma, Deepak Elmadjian, Carlos Raman, Balasubramanian Turk, Matthew Univ Calif Santa Barbara Santa Barbara CA 93106 USA Indian Inst Technol Roorkee Uttar Pradesh India Univ Sao Paulo Sao Paulo Brazil Raman Classes Roorkee Uttar Pradesh India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Producing sports highlights is a labor-intensive work that requires some degree of specialization. We propose a model capable of automatically generating sports highlights with a focus on cricket. Cricket is a sport with a complex set of rules and is played for a longer time than most other sports. In this paper we propose a model that considers both event-based and excitement-based features to recognize and clip important events in a cricket match. Replays, audio intensity, player celebration, and playfield scenarios are examples of cues used to capture such events. To evaluate our framework, we conducted a set of experiments ranging from user studies to a comparison analysis between our highlights and the ones provided by the official broadcasters. The general approval by users and the significant overlap between both kinds of highlights indicate the usefulness of our model in real-life scenarios.

关键词： Sports Games Hidden Markov models Support vector machines Optical character recognition software Cameras Training

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A Deep Face Identification Network Enhanced by Facial Attributes Prediction 31

A Deep Face Identification Network Enhanced by Facial Attrib...

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

作者： Taherkhani, Fariborz Nasrabadi, Nasser M. Dawson, Jeremy West Virginia Univ Morgantown WV 26506 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance. Our model is an end to end framework which consists of a convolutional neural network (CNN) whose output is fanned out into two separate branches;the first branch predicts facial attributes while the second branch identifies face images. Contrary to the existing multi-task methods which only use a shared CNN feature space to train these two tasks jointly, we fuse the predicted attributes with the features from the face modality in order to improve the face identification performance. Experimental results show that our model brings benefits to both face identification as well as facial attribute prediction performance, especially in the case of identity facial attributes such as gender prediction. We tested our model on two standard datasets annotated by identities and face attributes. Experimental results indicate that the proposed model outperforms most of the current existing face identification and attribute prediction methods.

关键词： Face Facial features Task analysis Predictive models Face recognition Feature extraction Training

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