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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是121-130 订阅

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Cross-Iteration Batch Normalization

Cross-Iteration Batch Normalization

引用

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

作者： Yao, Zhuliang Cao, Yue Zheng, Shuxin Huang, Gao Lin, Stephen Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

A well-known issue of Batch Normalization is its significantly reduced effectiveness in the case of small mini-batch sizes. When a mini-batch contains few examples, the statistics upon which the normalization is defined cannot be reliably estimated from it during a training iteration. To address this problem, we present Cross-Iteration Batch Normalization (CBN), in which examples from multiple recent iterations are jointly utilized to enhance estimation quality. A challenge of computing statistics over multiple iterations is that the network activations from different iterations are not comparable to each other due to changes in network weights. We thus compensate for the network weight changes via a proposed technique based on Taylor polynomials, so that the statistics can be accurately estimated and batch normalization can be effectively applied. On object detection and image classification with small mini-batch sizes, CBN is found to outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique.

关键词： Training computer vision Upper bound Codes Estimation Object detection pattern recognition

来源：评论

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引用

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

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781665445092

Deep convolutional neural networks (CNNs) leverage large-scale training dataset to produce remarkable performance on various image classification tasks. It, however, is difficult to effectively train the CNNs on some realistic learning situations such as regarding class imbalance, small-scale and label noises. Regularizing CNNs works well on learning with such deteriorated training datasets by mitigating overfitting issues. In this work we propose a method to effectively impose regularization on feature representation learning. By focusing on the angle between a feature and a classifier which is embedded in cosine similarity at the classification layer, we formulate a novel similarity beyond the cosine based on von Mises-Fisher distribution of directional statistics. In contrast to the cosine similarity, our similarity is compact while having heavy tail, which contributes to regularizing intra-class feature distribution to improve generalization performance. Through the experiments on some realistic learning situations such as of unbalance, small-scale and noisy labels, we demonstrate the effectiveness of the proposed method for training CNNs, in comparison to the other regularization methods. Codes are available at https://***/tk1980/tvMF.

关键词： Training computer vision Computational modeling Focusing pattern recognition Noise measurement Convolutional neural networks

来源：评论

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MOOD: Multi-level Out-of-distribution Detection

MOOD: Multi-level Out-of-distribution Detection

引用

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

作者： Lin, Ziqian Roy, Sreya Dutta Li, Yixuan Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781665445092

Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full feedforward pass for any given input. In this paper, we propose a novel framework, multi-level out-of-distribution detection (MOOD), which exploits intermediate classifier outputs for dynamic and efficient OOD inference. We explore and establish a direct relationship between the OOD data complexity and optimal exit level, and show that easy OOD examples can be effectively detected early without propagating to deeper layers. At each exit, the OOD examples can be distinguished through our proposed adjusted energy score, which is both empirically and theoretically suitable for networks with multiple classifiers. We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of complexities. Experiments demonstrate that MOOD achieves up to 71.05% computational reduction in inference, while maintaining competitive OOD detection performance.

关键词： computer vision Mood Complexity theory pattern recognition Computational efficiency

来源：评论

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Convolutional Hough Matching Networks

Convolutional Hough Matching Networks

引用

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

作者： Min, Juhong Cho, Minsu POSTECH CSE Pohang South Korea POSTECH GSAI Pohang South Korea

ISBN: (纸本)9781665445092

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

关键词： Visualization computer vision Semantics Neural networks Transforms Benchmark testing Robustness

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Optimal Quantization using Scaled Codebook

Optimal Quantization using Scaled Codebook

引用

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

作者： Idelbayev, Yerlan Molchanov, Pavlo Shen, Maying Yin, Hongxu Carreira-Perpinan, Miguel A. Alvarez, Jose M. Univ Calif Merced CA 95343 USA NVIDIA Corp Santa Clara CA USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled. The problem is defined as finding the optimal scaling factor a and the datapoint assignments into the a-scaled codebook to minimize the squared error between original and quantized points. Previously, the globally optimal algorithms for this problem were derived only for certain codebooks (binary and ternary) or under the assumption of certain distributions (Gaussian, Laplacian). By studying the properties of the optimal quantizer, we derive an O(NK logK) algorithm that is guaranteed to find the optimal quantization parameters for any fixed codebook regardless of data distribution. We apply our algorithm to synthetic and real-world neural network quantization problems and demonstrate the effectiveness of our approach.

关键词： computer vision Quantization (signal) Image coding Laplace equations Transform coding pattern recognition Image storage

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

引用

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

作者： Yang, Ren Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665448994

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.

关键词： computer vision Databases conferences Benchmark testing Solids pattern recognition

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Rich features for perceptual quality assessment of UGC videos

Rich features for perceptual quality assessment of UGC video...

引用

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

作者： Wang, Yilin Ke, Junjie Talebi, Hossein Yim, Joong Gon Birkbeck, Neil Adsumilli, Balu Milanfar, Peyman Yang, Feng Google Inc Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively investigate characteristics of generic UGC video quality. Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality. Our model is able to provide quality scores as well as human-friendly quality indicators, to bridge the gap between low level video signals to human perceptual quality. Experimental results show that our model achieves state-ofthe-art correlation with Mean Opinion Scores (MOS).

关键词： Industries computer vision Correlation User-generated content Quality assessment pattern recognition Video recording

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

引用

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

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Incremental Few-Shot Instance Segmentation

Incremental Few-Shot Instance Segmentation

引用

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

作者： Ganea, Dan Andrei Boom, Bas Poppe, Ronald Univ Utrecht Utrecht Netherlands Cyclomedia Technol Zaltbommel Netherlands

ISBN: (纸本)9781665445092

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly(1).

关键词： Location awareness Training Image segmentation computer vision Memory management Training data Transfer functions

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Sequence-to-Sequence Contrastive Learning for Text recognition

Sequence-to-Sequence Contrastive Learning for Text Recogniti...

引用

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

作者： Aberdam, Aviad Litman, Ron Tsiper, Shahar Anschel, Oron Slossberg, Ron Mazor, Shai Manmatha, R. Perona, Pietro Technion Haifa Israel AWS Seattle WA USA CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781665445092

We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to contrast in a sub-word level, where from each image we extract several positive pairs and multiple negative examples. To yield effective visual representations for text recognition, we further suggest novel augmentation heuristics, different encoder architectures and custom projection heads. Experiments on handwritten text and on scene text show that when a text decoder is trained on the learned representations, our method outperforms non-sequential contrastive methods. In addition, when the amount of supervision is reduced, SeqCLR significantly improves performance compared with supervised training, and when fine-tuned with 100% of the labels, our method achieves state-of-the-art results on standard handwritten text recognition benchmarks.

关键词： Training Visualization computer vision Head Text recognition computer architecture Benchmark testing

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