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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

引用

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

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

引用

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

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

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

作者： Gladisch, Christoph Heinzemann, Christian Herrmann, Martin Woehrle, Matthias Robert Bosch GmbH Corp Res Gerlingen Germany

ISBN: (纸本)9781728193601

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Object detection

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Fixation and Saccade based Face recognition from Single Image per Person with Various Occlusions and Expressions

Fixation and Saccade based Face Recognition from Single Imag...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Wei, Xingjie Li, Chang-Tsun Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England

ISBN: (纸本)9780769549903

Face recognition technique is widely used in the real-world applications over the past decade. Different from other biometric traits such as fingerprint and iris, face is the biological nature for humans to recognise a person even met just once. In this paper, we propose a novel method, which simulates the mechanism of fixations and saccades in human visual perception, to handle the face recognition from single image per person problem. Our method is robust to the local deformations of the face (i.e., expression changes and occlusions). Especially for the occlusion related problems, which have not received enough attentions compared with other challenging variations of illumination, expression and pose, our method significantly outperforms the state-of-the-art approaches despite various types of occlusions. Experimental results on the FRGC and the AR databases confirm the effectiveness of our method.

关键词： computer graphics face recognition

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End-to-end Optimized Video Compression with MV-Residual Prediction

End-to-end Optimized Video Compression with MV-Residual Pred...

引用

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

作者： Wu, XiangJi Zhang, Ziwen Feng, Jie Zhou, Lei Wu, Junmin Tucodec Inc Shanghai Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior auto-encoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.

关键词： Image coding Video compression Convolution Kernel computer vision conferences pattern recognition

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Cross-domain fashion image retrieval 31

Cross-domain fashion image retrieval

引用

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

作者： Gajic, Bojana Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edifici O UAB Bellaterra Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.

关键词： Task analysis Streaming media computer vision Image retrieval Biological system modeling conferences Training

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Temporal Segmentation and Activity Classification from First-person Sensing

Temporal Segmentation and Activity Classification from First...

引用

IEEE-computer-Society conference on computer vision and pattern recognition workshops

作者： Spriggs, Ekaterina H. De La Torre, Fernando Hebert, Martial Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781424439942

Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and Periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems - classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and Inertial Measurement Units (IMUs)for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-Multimodal activity database (CMU-MMAC).

关键词： computer vision

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When Kernel Methods meet Feature Learning: Log-Covariance Network for Action recognition from Skeletal Data 30

When Kernel Methods meet Feature Learning: Log-Covariance Ne...

引用

30th IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cavazza, Jacopo Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Genoa Electr Elect & Telecommun Engn Naval Architecture Genoa Italy Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538607336

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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A Holistic Framework for Addressing the World using Machine Learning 31

A Holistic Framework for Addressing the World using Machine ...

引用

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

作者： Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Raskar, Ramesh Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Facebook Menlo Pk CA 94025 USA MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.

关键词： Roads Urban areas Satellites Image segmentation computer vision conferences Topology

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