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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是261-270 订阅

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

引用

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

作者： Ramon, Raz Cohen-Duwek, Hadar Tsur, Elishai Ezra Open Univ Israel Dept Math & Comp Sci Neurobiomorph Engn Lab NBEL Raanana Israel

ISBN: (纸本)9798350365474

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multidomain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： event camera Neuromorphic vision object tracking

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Disguised Faces in the Wild 31

Disguised Faces in the Wild

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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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DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval

DAtRNet: Disentangling Fashion Attribute Embedding for Subst...

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

作者： Bhattacharya, Gaurab Kilari, Nikhil Gubbi, Jayavardhana Lakshmi, Bagya, V Pal, Arpan Balamuralidhar, P. TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive substitute recommendation for fashion products improves the online retail customer experience. Traditional fashion search platforms incorporate product metadata between the query products and the products to be retrieved. In this paper, we propose DAtRNet, an attribute representation network to disentangle the features in the query product. It is used to recommend attribute-aware substitute items based on the conditional similarity of the retrieved products. The proposed architecture relies on attribute-level similarity providing a fine-grained recommendation. In addition, a concurrent axial attention mechanism is proposed that generates global information embedding and adaptively re-calibrates the soft attention masks. Overall, the end-to-end framework enables the system to disentangle the attribute features and independently deals with them to enhance its flexibility towards one or multiple attributes. The proposed method outperforms the state-ofthe-art by a significant margin.

关键词： computer vision conferences computer architecture Metadata Search problems Feature extraction pattern recognition

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

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

作者： Mall, Utkarsh Hariharan, Bharath Bala, Kavita Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning (ZSL) tackles the problem of recognizing unseen classes using only semantic descriptions, e.g., attributes. Current zero-shot learning techniques all assume that a single vector of attributes suffices to describe each category. We show that this assumption is incorrect. Many classes in real world problems have multiple modes of appearance: male and female birds vary in appearance, for instance. Domain experts know this and can provide attribute descriptions of the chief modes of appearance for each class. Motivated by this, we propose the task of multi-modal zero-shot learning, where the learner must learn from these multimodal attribute descriptions. We present a technique for addressing this problem of multimodal ZSL that outperforms the unimodal counterpart significantly. We posit that multimodal ZSL is more practical for real-world problems where complex intra-class variation is common.

关键词： computer vision conferences Computational modeling Semantics Manuals Benchmark testing Birds

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PAND: Precise Action recognition on Naturalistic Driving

PAND: Precise Action Recognition on Naturalistic Driving

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

作者： Zhao, Hangyue Xiao, Yuchao Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Temporal action localization for untrimmed videos is a difficult problem in computer vision. It is challenge to infer the start and end of activity instances on small-scale datasets covering multi-view information accurately. In this paper, we propose an effective activity temporal localization and classification method to localize the temporal boundaries and predict the class label of activities for naturalistic driving. Our approach includes (i) a distraction behavior recognition and localization method in naturalistic driving videos on small-scale data sets, (ii) a strategy that uses multi-branch network to make full use of information from different channels, (iii)a post-processing method for selecting and correcting temporal range to ensure that our system finds accurate boundaries. In addition, the frame-level object detection information is also utilized. Extensive experiments prove the effectiveness of our method and we rank the 6th on the Test-A2 of the 6th AI City Challenge track 3.

关键词： Location awareness computer vision Urban areas Pipelines Object detection Data models pattern recognition

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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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Why Object Detectors Fail: Investigating the Influence of the Dataset

Why Object Detectors Fail: Investigating the Influence of th...

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

作者： Miller, Dimity Goode, Georgia Bennie, Callum Moghadam, Peyman Jurdak, Raja Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Robot & Autonomous Syst Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A false negative in object detection describes an object that was not correctly localised and classified by a detector. In prior work, we introduced five 'false negative mechanisms' that identify the specific component inside the detector architecture that failed to detect the object. Using these mechanisms, we explore how different computer vision datasets and their inherent characteristics can influence object detector failures. Specifically, we investigate the false negative mechanisms of Faster R-CNN and RetinaNet across five computer vision datasets, namely Microsoft COCO, Pascal VOC, ExDark, ObjectNet, and COD10K. Our results show that object size and class influence the false negative mechanisms of object detectors. We also show that comparing the false negative mechanisms of a single object class across different datasets can highlight potentially unknown biases in datasets.

关键词： computer vision conferences Detectors Object detection computer architecture Market research pattern recognition

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DNAS:A Decoupled Global Neural Architecture Search Method

DNAS:A Decoupled Global Neural Architecture Search Method

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

作者： Xu, Kepeng He, Gang Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) can automatically design model architecture with better performance. Current researchers have searched for local architecture similar to block, then stacked to construct entire models, or searched the entire model based on a manually designed benchmark module. There is no method to directly search the architecture of the global(entire) model at the operation level. The purpose of this article is to search the entire model directly in the operation level search space. We analyzed the search space of past methods which searching for local architectures, then a working mode for global model architecture search named CAM is proposed. Proposed CAM decouples the architectural parameters of the entire model which can complete the entire model architecture search with few architecture parameters. In the experiment, the test error 2.68 % in CIFAR-10 is obtained by the proposed method at the global architecture level, which can compare with the stage-of-art local architecture search methods.

关键词： Analytical models computer vision Search methods conferences computer architecture Benchmark testing pattern recognition

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Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN

Conjugate Adder Net (CAddNet) - a Space-Efficient Approximat...

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

作者： Shen, Lulan Ziaeefard, Maryam Meyer, Brett Gross, Warren Clark, James J. McGill Univ Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.

关键词： Training Deep learning conferences Neural networks Logic gates Complexity theory pattern recognition

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

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

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

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