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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Recurrent Segmentation for Variable Computational Budgets 31

Recurrent Segmentation for Variable Computational Budgets

引用

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

作者： McIntosh, Lane Maheswaranathan, Niru Sussillo, David Shlens, Jonathon Stanford Univ Stanford CA 94305 USA Google Brain Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods.

关键词： Image segmentation Semantics computer architecture Computational efficiency Videos Computational modeling Network architecture

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Human Pose as Calibration pattern;3D Human Pose Estimation with Multiple Unsynchronized and Uncalibrated Cameras 31

Human Pose as Calibration Pattern;3D Human Pose Estimation w...

引用

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

作者： Takahashi, Kosuke Mikami, Dan Isogawa, Mariko Kimata, Hideaki NIPPON TELEGRAPH & TEL COORPORAT NTT Media Intelligence Labs Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper proposes a novel algorithm of estimating 3D human pose from multi-view videos captured by unsynchronized and uncalibrated cameras. In a such configuration, the conventional vision-based approaches utilize detected 2D features of common 3D points for synchronization and camera pose estimation, however, they sometimes suffer from difficulties of feature correspondences in case of wide baselines. For such cases, the proposed method focuses on that the projections of human joints can be associated each other robustly even in wide baseline videos and utilizes them as the common reference points. To utilize the projections of joint as the corresponding points, they should be detected in the images, however, these 2D joint sometimes include detection errors which make the estimation unstable. For dealing with such errors, the proposed method introduces two ideas. The first idea is to relax the reprojection errors for avoiding optimizing to noised observations. The second idea is to introduce an geometric constraint on the prior knowledge that the reference points consists of human joints. We demonstrate the performance of the proposed algorithm of synchronization and pose estimation with qualitative and quantitative evaluations using synthesized and real data.

关键词： Cameras Two dimensional displays Three-dimensional displays Pose estimation Videos Calibration

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Efficient Semantic Segmentation using Gradual Grouping 31

Efficient Semantic Segmentation using Gradual Grouping

引用

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

作者： Vallurupalli, Nikitha Annamaneni, Sriharsha Varma, Girish Jawahar, C., V Mathew, Manu Nagori, Soyeb IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India Texas Instruments Inc Bangalore Karnataka India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Deep CNNs for semantic segmentation have high memory and run time requirements. Various approaches have been proposed to make CNNs efficient like grouped, shuffled, depth-wise separable convolutions. We study the effectiveness of these techniques on a real-time semantic segmentation architecture like ERFNet for improving run-time by over 5X. We apply these techniques to CNN layers partially or fully and evaluate the testing accuracies on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs, giving accuracy scores for models that can run under specified runtime budgets. We further propose a novel training procedure which starts out with a dense convolution but gradually evolves towards a grouped convolution. We show that our proposed training method and efficient architecture design can improve accuracies by over 8% with depthwise separable convolutions applied on the encoder of ERFNet and attaching a light weight decoder. This results in a model which has a 5X improvement in FLOPs while only suffering a 4% degradation in accuracy with respect to ERFNet.

关键词： Training Semantics computer architecture Computational modeling Sparse matrices Predictive models Decoding

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Pseudo-labels for Supervised Learning on Dynamic vision Sensor Data, Applied to Object Detection under Ego-motion 31

Pseudo-labels for Supervised Learning on Dynamic Vision Sens...

引用

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

作者： Chen, Nicholas F. Y. DSO Natl Labs 12 Sci Pk Dr Singapore 118225 Singapore

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.

关键词： vision sensors Cameras Vehicle dynamics Object detection Voltage control Supervised learning Automobiles

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Temporal Alignment Improves Feature Quality: an Experiment on Activity recognition with Accelerometer Data 31

Temporal Alignment Improves Feature Quality: an Experiment o...

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

作者： Choi, Hongjun Wang, Qiao Toledo, Meynard Turaga, Pavan Buman, Matthew Srivastava, Anuj Arizona State Univ Geometr Media Lab Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Sch Nutr & Hlth Promot Tempe AZ 85287 USA Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Activity recognition has been receiving significant attention from a variety of research areas such as human performance enhancement, health promotion, and human computer interaction. However, recognizing activities from accelerometer data still remains a challenging problem due to sensitivity to sampling rates, misalignment of data, and increased variability in activities among clinically relevant populations. In order to solve these issues, we adopt methods from functional analysis, which consider non-elastic rate variations in movement. The overall framework factors out temporal variability within activity classes, before leveraging robust machine learning pipelines for a given end-use. The proposed approach has been evaluated on 7 classes of everyday activities with 50 subjects. The results indicate that proposed approach achieves improved performance with the improvements observed in separating similar classes that differ in temporal rates, and also demonstrate higher robustness to change in window lengths. These results suggest that temporal alignment should be considered a core part of activity recognition pipelines.

关键词： Feature extraction Activity recognition Microsoft Windows Machine learning Pipelines Training Indexes

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Hard Example Mining with Auxiliary Embeddings 31

Hard Example Mining with Auxiliary Embeddings

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

作者： Smirnov, Evgeny Melnikov, Aleksandr Oleinik, Andrei Ivanova, Elizaveta Kalinovskiy, Ilya Luckyanets, Eugene Speech Technol Ctr Moscow Russia ITMO Univ St Petersburg Russia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Hard example mining is an important part of the deep embedding learning. Most methods perform it at the mini-batch level. However, in the large-scale settings there is only a small chance that proper examples will appear in the same mini-batch and will be coupled into the hard example pairs or triplets. Doppelganger mining was previously proposed to increase this chance by means of class-wise similarity. This method ensures that examples of similar classes are sampled into the same mini-batch together. One of the drawbacks of this method is that it operates only at the class level, while there also might be a way to select appropriate examples within class in a more elaborated way than randomly. In this paper, we propose to use auxiliary embeddings for hard example mining. These embeddings are constructed in such way that similar examples have close embeddings in the cosine similarity sense. With the help of these embeddings it is possible to select new examples for the mini-batch based on their similarity with the already selected examples. We propose several ways to create auxiliary embeddings and use them to increase the number of potentially hard positive and negative examples in each minibatch. Our experiments on the challenging Disguised Faces in the Wild (DFW) dataset show that hard example mining with auxiliary embeddings improves the discriminative power of learned representations.

关键词： Training Prototypes Measurement Speech recognition Face recognition Neural networks Image recognition

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WiCV 2019: The Sixth Women In computer vision Workshop

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

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

作者： Irene Amerini Elena Balashova Sayna Ebrahimi Kathryn Leonard Arsha Nagrani Amaia Salvador University of Florence Princeton University UC Berkeley Occidental College University of Oxford Universitat Politcnica de Catalunya

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

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： conferences computer vision Machine learning Engineering profession Facebook Buildings

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A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving 31

A Comparative Study of Real-time Semantic Segmentation for A...

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

作者： Siam, Mennatullah Gamal, Mostafa Abdel-Razek, Moemen Yogamani, Senthil Jagersand, Martin Zhang, Hong Univ Alberta Edmonton AB Canada Cairo Univ Giza Egypt Valeo Vis Syst Tuam Ireland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is a critical module in robotics related applications, especially autonomous driving. Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions. Majority of the efficient semantic segmentation algorithms have customized optimizations without scalability and there is no systematic way to compare them. In this paper, we present a real-time segmentation benchmarking framework and study various segmentation algorithms for autonomous driving. We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently. We provide several example encoders including VGG16, Resnet18, MobileNet, and ShuffleNet and decoders including SkipNet, UNet and Dilation Frontend. The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks. We performed detailed experimental analysis on cityscapes dataset for various combinations of encoder and decoder. The modular framework enabled rapid prototyping of a custom efficient architecture which provides similar to x143 GFLOPs reduction compared to SegNet and runs real-time at similar to 15 fps on NVIDIA Jetson TX2. The source code of the framework is publicly available(1).

关键词： Convolution Semantics computer architecture Decoding Context modeling Real-time systems Image segmentation

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Empirically Analyzing the Effect of Dataset Biases on Deep Face recognition Systems 31

Empirically Analyzing the Effect of Dataset Biases on Deep F...

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

作者： Kortylewski, Adam Egger, Bernhard Schneider, Andreas Gerig, Thomas Morel-Forster, Andreas Vetter, Thomas Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.

关键词： Face Face recognition Lighting Generators Training data computer architecture Task analysis

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Predicting Dynamical Evolution of Human Activities from a Single Image 31

Predicting Dynamical Evolution of Human Activities from a Si...

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

作者： Lohit, Suhas Bansal, Ankan Shroff, Nitesh Pillai, Jaishanker Turaga, Pavan Chellappa, Rama Arizona State Univ Geometr Media Lab Tempe AZ 85281 USA Univ Maryland Ctr Automat Res UMIACS College Pk MD 20742 USA Zoox Inc Foster City CA USA Google Res Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

A human pose often conveys not only the configuration of the body parts, but also implicit predictive information about the ensuing motion. This dynamic information can benefit vision applications which lack explicit motion cues. The human visual system can easily perceive the dynamic information in still images. However, computational algorithms to infer and utilize it in computer vision applications are limited. In this paper, we propose a probabilistic framework to infer the dynamic information associated with a human pose. The inference problem is posed as a non-parametric density estimation problem on a non-Euclidean manifold of linear dynamical models. Since direct modeling is intractable, we develop a data driven approach, estimating the density for the test sample under consideration. Statistical inference on the estimated density provides us with quantities of interest like the most probable future motion of the human and the amount of motion information conveyed by a pose. Our experiments demonstrate that the extracted motion information benefits numerous applications in computer vision. In particular, the predicted future motion is useful for activity recognition, human trajectory synthesis, and motion prediction.

关键词： Dynamics Motion segmentation Trajectory Uncertainty Training Activity recognition Computational modeling

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