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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015"

共 4931 条记录，以下是101-110 订阅

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Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Revisiting The Evaluation of Class Activation Mapping for Ex...

引用

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

作者： Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781665448994

As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.

关键词： Deep learning Visualization computer vision Protocols conferences Reproducibility of results pattern recognition

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BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision

BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...

引用

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

作者： Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

ISBN: (纸本)9781665448994

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.

关键词： Training computer vision Convolution conferences Neural networks Memory management Decoding

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Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

Neural Architecture Search of Deep Priors: Towards Continual...

引用

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

作者： Mundt, Martin Pliushch, Iuliia Ramesh, Visvanathan Goethe Univ Frankfurt Germany

ISBN: (纸本)9781665448994

In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.

关键词： Training computer vision conferences Neural networks computer architecture Interference pattern recognition

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Towards Explaining Image-Based Distribution Shifts

Towards Explaining Image-Based Distribution Shifts

引用

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

作者： Kulinski, Sean Inouye, David I. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding such distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on either natively handling distribution shift (e.g., Domain Generalization) or merely detecting a shift while assuming any detected shift can be understood and handled appropriately by a human operator. For the latter, we hope to aid in these manual mitigation tasks by explaining the distribution shift to an operator. To this end, we suggest two methods: providing a set of interpretable mappings from the original distribution to the shifted one or providing a set of distributional counterfactual examples. We provide preliminary experiments on these two methods, and discuss important concepts and challenges for moving towards a better understanding of image-based distribution shifts.

关键词： computer vision conferences pattern recognition Task analysis

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Compositional Mixture Representations for vision and Text

Compositional Mixture Representations for Vision and Text

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

作者： Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

关键词： Representation learning Visualization Computational modeling Semantics Image retrieval Employment Object detection

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Comparison of deep transfer learning strategies for digital pathology 31

Comparison of deep transfer learning strategies for digital ...

引用

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

作者： Mormont, Romain Geurts, Pierre Maree, Raphael Univ Liege Liege Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

关键词： Feature extraction Task analysis Biomedical imaging Training Support vector machines computer vision

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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Variational Autoencoders for Generating Hyperspectral Imagin...

引用

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

作者： Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.

关键词： Training computer vision conferences Training data Machine learning Detectors pattern recognition

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Robustness and Adaptation to Hidden Factors of Variation

Robustness and Adaptation to Hidden Factors of Variation

引用

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

作者： Paul, William Burlina, Philippe Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data. Towards this end, we employ a two step strategy that a) does unsupervised discovery, via generative models, of sensitive factors that cause models to under-perform, and b) intervenes models to make their performance invariant to these sensitive factors' influence. We consider 3 separate interventions for robustness, including: data augmentation, semantic consistency, and adversarial alignment. We evaluate our method using metrics that measure trade offs between invariance (insensitivity) and overall performance (utility) and show the benefits of our method for 3 settings (unsupervised, semi-supervised and generalization).

关键词： Measurement computer vision conferences Semantics Robustness Data models pattern recognition

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Key Point-Based Driver Activity recognition

Key Point-Based Driver Activity Recognition

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

作者： Vats, Arpita Anastasiu, David C. Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Pose estimation Activity recognition Predictive models Feature extraction

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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

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

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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