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检索条件"任意字段=32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019"

共 858 条记录，以下是31-40 订阅

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Min-Max Statistical Alignment for Transfer Learning 32

Min-Max Statistical Alignment for Transfer Learning

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Herath, Samitha Harandi, Mehrtash Fernando, Basura Nock, Richard Australian Natl Univ Canberra ACT Australia Monash Univ Clayton Vic Australia Univ Sydney Sydney NSW Australia CSIRO DATA61 Canberra ACT Australia ASTAR Human Ctr AI Programme Singapore Singapore

ISBN: (纸本)9781728132938

A profound idea in learning invariant features for transfer learning is to align statistical properties of the domains. In practice, this is achieved by minimizing the disparity between the domains, usually measured in terms of their statistical properties. We question the capability of this school of thought and propose to minimize the maximum disparity between domains. Furthermore, we develop an end-to end learning scheme that enables us to benefit from the proposed min-max strategy in training deep models. We show that the min-max solution can outperform the existing statistical alignment solutions, and can compete with state-of-the-art solutions on two challenging learning tasks, namely, Unsupervised Domain Adaptation (UDA) and Zero-Shot Learning (ZSL).

关键词： Categorization recognition: Detection Representation Learning Retrieval

来源：评论

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Bag of Tricks for Image Classification with Convolutional Neural Networks 32

Bag of Tricks for Image Classification with Convolutional Ne...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Tong Zhang, Zhi Zhang, Hang Zhang, Zhongyue Xie, Junyuan Li, Mu Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781728132938

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

关键词： Categorization Deep Learning recognition: Detection Retrieval

来源：评论

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vision-Based Navigation With Language-Based Assistance via Imitation Learning With Indirect Intervention 32

Vision-Based Navigation With Language-Based Assistance via I...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Nguyen, Khanh Dey, Debadeepta Brockett, Chris Dolan, Bill Univ Maryland College Pk MD 20742 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781728132938

We present vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level end-goals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines on both seen and unseen environments.

关键词： Representation Learning Robotics + Driving vision + Language Visual Reasoning

来源：评论

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A General and Adaptive Robust Loss Function 32

A General and Adaptive Robust Loss Function

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Barron, Jonathan T. Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781728132938

We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, our loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting our loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.

关键词： 3D from Single Image computer vision Theory Deep Learning Image and Video Synthesis Low-level vision Statistic

来源：评论

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Probabilistic End-to-end Noise Correction for Learning with Noisy Labels 32

Probabilistic End-to-end Noise Correction for Learning with ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yi, Kun Wu, Jianxin Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781728132938

Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions. PENCIL is independent of the backbone network structure and does not need an auxiliary clean dataset or prior information about noise, thus it is more general and robust than existing methods and is easy to apply. PENCIL out performs previous state-of-the-art methods by large margins on both synthetic and real-world datasets with different noise types and noise rates. Experiments show that PENCIL is robust on clean datasets, too.

关键词： Categorization Deep Learning Others recognition: Detection Retrieval

来源：评论

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Dense Classification and Implanting for Few-Shot Learning 32

Dense Classification and Implanting for Few-Shot Learning

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lifchitz, Yann Avrithis, Yannis Picard, Sylvaine Bursuc, Andrei Univ Rennes INRIA CNRS IRISA Rennes France Safran Paris France Valeo Ai Paris France

ISBN: (纸本)9781728132938

Few-shot learning for deep neural networks is a highly challenging and key problem in many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. Implanting enables training of multiple layers in the few-shot regime, departing from most related methods derived from metric learning that train only the final layer. Both contributions show consistent gains when used individually or jointly and we report state of the art performance on few-shot classification on minilmageNet.

关键词： Categorization Deep Learning recognition: Detection Retrieval

来源：评论

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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation 32

ADVENT: Adversarial Entropy Minimization for Domain Adaptati...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tuan-Hung Vu Jain, Himalaya Bucher, Maxime Cord, Matthieu Perez, Patrick Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9781728132938

Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real-world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging "synthetic-2-real" set-ups and show that the approach can also be used for detection.

关键词： Categorization Deep Learning Grouping and Shape recognition: Detection Retrieval Scene Analysis and Understanding Segmentation

来源：评论

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Few-Shot Learning with Localization in Realistic Settings 32

Few-Shot Learning with Localization in Realistic Settings

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wertheimer, Davis Hariharan, Bharath Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781728132938

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new meta-iNat benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.

关键词： Categorization Deep Learning Grouping and Shape recognition: Detection Retrieval Segmentation

来源：评论

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A neural network based on SPD manifold learning for skeleton-based hand gesture recognition 32

A neural network based on SPD manifold learning for skeleton...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Nguyen, Xuan Son Brun, Luc Lezoray, Olivier Bougleux, Sebastien Normandie Univ GREYC CNRS ENSICAENUNICAEN F-14000 Caen France

ISBN: (纸本)9781728132938

This paper proposes a new neural network based on SPD manifold learning for skeleton-based hand gesture recognition. Given the stream of hand's joint positions, our approach combines two aggregation processes on respectively spatial and temporal domains. The pipeline of our network architecture consists in three main stages. The first stage is based on a convolutional layer to increase the discriminative power of learned features. The second stage relies on different architectures for spatial and temporal Gaussian aggregation of joint features. The third stage learns a final SPD matrix from skeletal data. A new type of layer is proposed for the third stage, based on a variant of stochastic gradient descent on Stiefel manifolds. The proposed network is validated on two challenging datasets and shows state-of-the-art accuracies on both datasets.

关键词： Action recognition And Body Pose Deep Learning Face Gesture

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LSTA: Long Short-Term Attention for Egocentric Action recognition 32

LSTA: Long Short-Term Attention for Egocentric Action Recogn...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sudhakaran, Swathikiran Escalera, Sergio Lanz, Oswald Fdn Bruno Kessler Trento Italy Univ Trento Trento Italy Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9781728132938

Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from relevant spatial parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state-of-the-art performance on four standard benchmarks.

关键词： Action recognition Deep Learning

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