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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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On the Suitability of L_p-norms for Creating and Preventing Adversarial Examples 31

On the Suitability of <i>L<sub>p</sub></i>-norms for Creatin...

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

作者： Sharif, Mahmood Bauer, Lujo Reiter, Michael K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Much research has been devoted to better understanding adversarial examples, which are specially crafted inputs to machine-learning models that are perceptually similar to benign inputs, but are classified differently (i.e., misclassified). Both algorithms that create adversarial examples and strategies for defending against adversarial examples typically use L-p-norms to measure the perceptual similarity between an adversarial input and its benign original. Prior work has already shown, however, that two images need not be close to each other as measured by an L-p-norm to be perceptually similar. In this work, we show that nearness according to an L-p-norm is not just unnecessary for perceptual similarity, but is also insufficient. Specifically, focusing on datasets (CIFAR10 and MNIST), L-p-norms, and thresholds used in prior work, we show through online user studies that "adversarial examples" that are closer to their benign counterparts than required by commonly used L-p-norm thresholds can nevertheless be perceptually distinct to humans from the corresponding benign examples. Namely, the perceptual distance between two images that are "near" each other according to an L-p-norm can be high enough that participants frequently classify the two images as representing different objects or digits. Combined with prior work, we thus demonstrate that nearness of inputs as measured by L-p-norms is neither necessary nor sufficient for perceptual similarity, which has implications for both creating and defending against adversarial examples. We propose and discuss alternative similarity metrics to stimulate future research in the area.

关键词： Machine learning Measurement Training computer vision Cancer Object recognition Face recognition

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Gait recognition by Deformable Registration 31

Gait Recognition by Deformable Registration

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

作者： Makihara, Yasushi Adachi, Daisuke Xu, Chi Yagi, Yasushi Osaka Univ Suita Osaka Japan Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper describes a method of gait recognition robust against intra-subject posture changes. A person sometimes walks with changing his/her posture when looking down at a smartphone or carrying a heavy object, which makes intra-subject variation large and consequently makes gait recognition difficult. We therefore introduce a deformable registration model to mitigate the intra-subject posture changes. More specifically, we represent a deformation field by a set of deformation vectors on lattice-type control points allocated on an image, i.e., by free-form deformation (FFD) framework. Given a pair of a probe and a gallery, we compute the deformation field so as to minimize the difference between a probe morphed by the deformation field and the gallery, as well as to ensure the spatial smoothness of the deformation field. We then learn the intra-subject eigen deformation modes from a training set of the same subjects' pairs (e.g., bending the upper body forward and swinging arms more), which are relatively different from inter-subject deformation modes (e.g., body shape spread and stride change). Moreover, because the deformable registration is responsible for a preprocessing part before matching, it can be combined with any types of matching algorithms for gait recognition. Experiments with 1,334 subjects show that the proposed method improves the gait recognition accuracy in both cases without and with a state-of-the-art deep learning-based matcher, respectively.

关键词： Strain Gait recognition Probes Deformable models Measurement Shape Computational modeling

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

ISBN: (纸本)9781467388511

The proceedings contain 626 papers. The topics discussed include: learning to match aerial images with deep attentive architectures; learnt quasi-transitive similarity for retrieval from large collections of faces; unsupervised learning from narrated instruction videos; action recognition in video using sparse coding and relative features; recognizing emotions from abstract paintings using non-linear matrix completion; rolling shutter absolute pose problem with known vertical direction; memory efficient max flow for multi-label submodular MRFs; recovering the missing link: predicting class-attribute associations for unsupervised zero-shot learning; neural module networks; multi-cue zero-shot learning with strong supervision; social LSTM: human trajectory prediction in crowded spaces; NetVLAD: CNN architecture for weakly supervised place recognition; 3D semantic parsing of large-scale indoor spaces; efficient indexing of billion-scale datasets of deep descriptors; analyzing classifiers: Fisher vectors and deep neural networks; and GIFT: a real-time and scalable 3d shape search engine.

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Dual-Mode Vehicle Motion pattern Learning for High Performance Road Traffic Anomaly Detection 31

Dual-Mode Vehicle Motion Pattern Learning for High Performan...

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

作者： Xu, Yan Ouyang, Xi Cheng, Yu Yu, Shining Xiong, Lin Ng, Choon-Ching Pranata, Sugiri Shen, Shengmei Xing, Junliang Panasonic R&D Ctr Singapore Singapore Singapore Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Nanyang Technol Univ Singapore Singapore Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Anomaly detection on road traffic is an important task due to its great potential in urban traffic management and road safety. It is also a very challenging task since the abnormal event happens very rarely and exhibits different behaviors. In this work, we present a model to detect anomaly in road traffic by learning from the vehicle motion patterns in two distinctive yet correlated modes, i.e., the static mode and the dynamic mode, of the vehicles. The static mode analysis of the vehicles is learned from the background modeling followed by vehicle detection procedure to find the abnormal vehicles that keep still on the road. The dynamic mode analysis of the vehicles is learned from detected and tracked vehicle trajectories to find the abnormal trajectory which is aberrant from the dominant motion patterns. The results from the dual-mode analyses are finally fused together by driven a re-identification model to obtain the final anomaly. Experimental results on the Track 2 testing set of NVIDIA AI CITY CHALLENGE show the effectiveness of the proposed dual-mode learning model and its robustness in different real scenes. Our result ranks the first place on the final Leaderboard of the Track 2.

关键词： Roads Automobiles Anomaly detection Vehicle dynamics Analytical models Tracking Surveillance

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Towards dense object tracking in a 2D honeybee hive 31

Towards dense object tracking in a 2D honeybee hive

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bozek, Katarzyna Hebert, Laetitia Mikheyev, Alexander S. Stephens, Greg J. OIST Grad Univ Okinawa Japan Vrije Univ Amsterdam Fac Sci Amsterdam Netherlands

ISBN: (纸本)9781538664209

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of similar to 7% of a typical body dimension, and orientation error of 12, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.

关键词： Labeling Video recording Object tracking Image recognition Image resolution Light emitting diodes

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Time Analysis of Pulse-based Face Anti-Spoofing in Visible and NIR 31

Time Analysis of Pulse-based Face Anti-Spoofing in Visible a...

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

作者： Hernandez-Ortega, Javier Fierrez, Julian Morales, Aythami Tome, Pedro Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper we study Presentation Attack Detection (PAD) in face recognition systems against realistic artifacts such as 3D masks or good quality of photo attacks. In recent works, pulse detection based on remote photo-plethysmography (rPPG) has shown to be a effective countermeasure in concrete setups, but still there is a need for a deeper understanding of when and how this kind of PAD works in various practical conditions. Related works analyze full video sequences (usually over 60 seconds) to distinguish between attacks and legitimate accesses. However, existing approaches may not be as effective as it has been claimed in the literature in time variable scenarios. In this paper we evaluate the performance of an existent state-of-the-art PAD scheme based on rPPG when analyzing short-time video sequences extracted from a longer video. Results are reported using the 3D Mask Attack Database (3DMAD), and a self-collected dataset called Heart Rate Database (HR), including different video durations, spectrum bands, resolutions and frame rates. Several conclusions can be drawn from this work: a) PAD performance based on rPPG varies significantly with the length of the analyzed video, b) rPPG information extracted from short-time sequences (over 5 seconds) can be discriminant enough for performing the PAD task, c) in general, videos using the NIR band perform better than those using the RGB band, and d) the temporal resolution is more valuable for rPPG signal extraction than the spatial resolution.

关键词： Face Three-dimensional displays Heart rate Databases Face recognition Video sequences Feature extraction

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On the iterative refinement of densely connected representation levels for semantic segmentation 31

On the iterative refinement of densely connected representat...

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

作者： Casanova, Arantxa Cucurull, Guillem Drozdzal, Michal Romero, Adriana Bengio, Yoshua Montreal Inst Learning Algorithms Montreal PQ Canada Comp Vis Ctr Barcelona Spain Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.

关键词： Semantics Spatial resolution computer architecture Convolutional codes Computational modeling computer vision Image segmentation

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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29th ieee conference on computer vision and pattern recognition, cvpr 2016

ISBN: (纸本)9781467388504

The proceedings contain 640 papers. The topics discussed include: generation and comprehension of unambiguous object description;image question answering using convolutional neural network with dynamic parameter prediction;neural module networks;learning to assign orientations to feature points;affinity CNN: learning pixel-centric pairwise relations for figure/ground embedding;occlusion boundary detection via deep exploration of context;exploit bounding box annotations for multi-label object recognition;MCMC shape sampling for image segmentation with nonparametric shape priors;learning action maps of large environments via first-person vision;sample and filter: nonparametric scene parsing via efficient filtering;training region-based object detectors with online hard example mining;learning with side information through modality hallucination;HyperNet: towards accurate region proposal generation and joint object detection;macroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flight;ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels;hierarchical recurrent neural encoder for video representation with application to captioning;and from keyframes to key objects: video summarization by representative object proposal selection.

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Recognizing American Sign Language Gestures from within Continuous Videos 31

Recognizing American Sign Language Gestures from within Cont...

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

作者： Ye, Yuancheng Tian, Yingli Huenerfauth, Matt Liu, Jingya CUNY Grad Ctr New York NY 10021 USA CUNY City Coll New York NY 10021 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose a novel hybrid model, 3D recurrent convolutional neural networks (3DRCNN), to recognize American Sign Language (ASL) gestures and localize their temporal boundaries within continuous videos, by fusing multi-modality features. Our proposed 3DRCNN model integrates 3D convolutional neural network (3DCNN) and enhanced fully connected recurrent neural network (FC-RNN), where 3DCNN learns multi-modality features from RGB, motion, and depth channels, and FC-RNN captures the temporal information among short video clips divided from the original video. Consecutive clips with the same semantic meaning are singled out by applying the sliding window approach to segment the clips on the entire video sequence. To evaluate our method, we collected a new ASL dataset which contains two types of videos: Sequence videos (in which a human performs a list of specific ASL words) and Sentence videos (in which a human performs ASL sentences, containing multiple ASL words). The dataset is fully annotated for each semantic region (i.e. the time duration of each word that the human signer performs) and contains multiple input channels. Our proposed method achieves 69.2% accuracy on the Sequence videos for 27 ASL words, which demonstrates its effectiveness of detecting ASL gestures from continuous videos.

关键词： Videos Assistive technology Gesture recognition Three-dimensional displays Task analysis Feature extraction Recurrent neural networks

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SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared 31

SparsePPG: Towards Driver Monitoring Using Camera-Based Vita...

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

作者： Nowara, Ewa Magdalena Marks, Tim K. Mansour, Hassan Veeraraghavan, Ashok MERL Cambridge MA 02139 USA Rice Univ Houston TX 77005 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Camera-based measurement of the heartbeat signal from minute changes in the appearance of a person's skin is known as remote photoplethysmography (rPPG). Methods for rPPG have improved considerably in recent years, making possible its integration into applications such as telemedicine. Driver monitoring using in-car cameras is another potential application of this emerging technology. Unfortunately, there are several challenges unique to the driver monitoring context that must be overcome. First, there are drastic illumination changes on the driver's face, both during the day (as sun filters in and out of overhead trees, etc.) and at night (from streetlamps and oncoming headlights), which current rPPG algorithms cannot account for: We argue that these variations are significantly reduced by narrow-bandwidth near-infrared (NIR) active illumination at 940 nm, with matching bandpass filter on the camera. Second, the amount of motion during driving is significant. We perform a preliminary analysis of the motion magnitude and argue that any in-car solution must provide better robustness to motion artifacts. Third, low signal-tonoise ratio (SNR) and false peaks due to motion have the potential to confound the rPPG signal. To address these challenges, we develop a novel rPPG signal tracking and denoising algorithm (sparsePPG) based on Robust Principal Components Analysis and sparse frequency spectrum estimation. We release a new dataset of face videos collected simultaneously in RGB and NIR. We demonstrate that in each of these frequency ranges, our new method performs as well as or better than current state-of-the-art rPPG algorithms. Overall, our preliminary study indicates that while driver vital signs monitoring using cameras is promising, much work needs to be done in terms of improving robustness to motion artifacts before it becomes practical.

关键词： computer vision pattern recognition conferences

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