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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture recognition 31

Motion Fused Frames: Data Level Fusion Strategy for Hand Ges...

引用

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

作者： Kopuklu, Okan Kose, Neslihan Rigoll, Gerhard Tech Univ Munich Inst Human Machine Commun Munich Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets -Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.

关键词： Optical imaging Feature extraction Gesture recognition Motion segmentation Optical sensors computer architecture Image color analysis

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

引用

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

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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WiCV 2018: The Fourth Women In computer vision Workshop 31

WiCV 2018: The Fourth Women In Computer Vision Workshop

引用

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

作者： Demir, Ilke Bazazian, Dena Romero, Adriana Sharmanska, Viktoriia Tchapmi, Lyne P. Facebook Menlo Pk CA 94025 USA Comp Vis Ctr Barcelona Spain Facebook AI Res Montreal PQ Canada Imperial Coll London London England Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present WiCV 2018 - Women in computer vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.

关键词： conferences computer vision Industries Object recognition Engineering profession Collaboration Machine learning

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

引用

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

作者： Cornia, Marcella Baraldi, Lorenzo Serra, Giuseppe Cmcidara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Udine Udine Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.

关键词： Predictive models Visualization Measurement Feature extraction conferences computer architecture Computational modeling

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Encapsulating the impact of transfer learning, domain knowledge and training strategies in deep-learning based architecture: A biometric based case study 31

Encapsulating the impact of transfer learning, domain knowle...

引用

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

作者： Singh, Avantika Nigam, Aditya Indian Inst Technol Mandi Mandi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K ( our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IIT-K. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.

关键词： Lenses Iris recognition Training Biological system modeling Knowledge engineering pattern recognition Analytical models

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Invariance to Affine-Permutation Distortions

Invariance to Affine-Permutation Distortions

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Liang-Yan Gui David A. Sepiashvili Jose M. F. Moura Carnegie Mellon University Independent Consulting

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

An object imaged from various viewpoints appears very different. Hence, effective shape representation of objects becomes central in many applications of computer vision. We consider affine and permutation distortions. We derive the affine-permutation shape space that extends, to include permutation distortions, the affine only shape space (the Grassmannian). We compute the affine-permutation shape space metric, the sample mean of multiple shapes, the geodesic defined by two shapes, and a canonical representative for a shape equivalence class. We illustrate our approach in several applications including clustering and morphing of shapes of different objects along a geodesic path. The experimental results on key benchmark datasets demonstrate the effectiveness of our framework.

关键词： images computer vision invariance Sample mean Geodesic Permutation In-law Shape Affine Equivalence classes

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A Novel Algorithm for Skeleton Extraction From Images Using Topological Graph Analysis

A Novel Algorithm for Skeleton Extraction From Images Using ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Liping Yang Diane Oyen Brendt Wohlberg Los Alamos National Laboratory

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Skeletonization, also called thinning, is an important pre-processing step in computer vision and image processing tasks such as shape analysis and vectorization. It is a morphological process that generates a skeleton from an input image. Many thinning algorithms have been proposed, but accurate and fast algorithms are still in demand. In this paper, we propose a novel algorithm using embedded topological graphs and computational geometry that can extract skeletons from input binary images. We compare three well-known thinning algorithms with our method, with the experimental results showing effectiveness of the proposed method and algorithms.

关键词： Skeletons images bone structure topological graphs algorithms image processing task Computational geometry shape analysis computer vision A* Algorithm singling Thinning THINNING morphological processing Binary images

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Multi-stage Optimization for Photorealistic Neural Style Transfer

Multi-stage Optimization for Photorealistic Neural Style Tra...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Richard R. Yang Department of Computer Science Stanford University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This work introduces a new approach toward photorealistic style transfer. When applying current style transfer techniques on real world photographs, the generated results often contain distortions and artifacts that diminish the real-world quality of the photograph. To address these issues, we propose a two-stage optimization process that transfers style globally and regionally and applies a sharpening filter after each step. As evaluated by a user study, our method is qualitatively comparable to existing state-of-the-art methods, but successfully handles previous failure cases. Our method also quantitatively outperform previous methods as evaluated by natural scene statistic metrics.

关键词： Photorealistic CASING FAILURE Optimization Procedure Photographs Steps

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Disguised Faces in the Wild 31

Disguised Faces in the Wild

引用

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

作者： Kushwaha, Vineet Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini Chellappa, Rama IIIT Delhi Delhi India IBM TJ Watson Res Ctr Ossining NY USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538661000

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.

关键词： computer vision pattern recognition conferences

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NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation 31

NU-Net: Deep Residual Wide Field of View Convolutional Neura...

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

作者： Samy, Mohamed Amer, Karim Eissa, Kareem Shaker, Mahmoud ElHelw, Mohamed Nile Univ Ctr Informat Sci Giza Egypt

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder's feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full resolution information. NU-Net is a deep FCNN that is able to capture wide field of view global information around each pixel while maintaining localized full resolution information throughout the model. We evaluate our model on the Land Cover Classification and Road Extraction tracks in the DeepGlobe competition.

关键词： Image segmentation Semantics Roads computer architecture Convolution computer vision pattern recognition

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