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Degree-of-linear-polarization-based Color Constancy

Degree-of-linear-polarization-based Color Constancy

引用

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

作者： Ono, Taishi Kondo, Yuhi Sun, Legong Kurita, Teppei Moriuchi, Yusuke Sony Grp Corp Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Color constancy is an essential function in digital photography and a fundamental process for many computer vision applications. Accordingly, many methods have been proposed, and some recent ones have used deep neural networks to handle more complex scenarios. However, both the traditional and latest methods still impose strict assumptions on their target scenes in explicit or implicit ways. This paper shows that the degree of linear polarization dramatically solves the color constancy problem because it allows us to find achromatic pixels stably. Because we only rely on the physics-based polarization model, we significantly reduce the assumptions compared to existing methods. Furthermore, we captured a wide variety of scenes with ground-truth illuminations for evaluation, and the proposed approach achieved state-of-the-art performance with a low computational cost. Additionally, the proposed method can estimate illumination colors from chromatic pixels and manage multi-illumination scenes. Lastly, the evaluation scenes and codes are publicly available to encourage more development in this field.

关键词： Deep learning computer vision Runtime Computational modeling Neural networks Lighting Color

来源：评论

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Arbitrary-Scale Image Synthesis

Arbitrary-Scale Image Synthesis

引用

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

作者： Ntavelis, Evangelos Shahbazi, Mohamad Kastanis, Iason Timofte, Radu Danelljan, Martin Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland CSEM Robot & ML Neuchatel Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis.

关键词： Training computer vision Image coding Image synthesis Pipelines Generative adversarial networks Encoding

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A Unified Framework for Implicit Sinkhorn Differentiation

A Unified Framework for Implicit Sinkhorn Differentiation

引用

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

作者： Eisenberger, Marvin Toker, Aysim Leal-Taixe, Laura Bernard, Florian Cremers, Daniel Tech Univ Munich Munich Germany Univ Bonn Bonn Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning frameworks. To allow for an efficient training of respective neural networks, we propose an algorithm that obtains analytical gradients of a Sinkhorn layer via implicit differentiation. In comparison to prior work, our framework is based on the most general formulation of the Sinkhorn operator. It allows for any type of loss function, while both the target capacities and cost matrices are differentiated jointly. We further construct error bounds of the resulting algorithm for approximate inputs. Finally, we demonstrate that for a number of applications, simply replacing automatic differentiation with our algorithm directly improves the stability and accuracy of the obtained gradients. Moreover, we show that it is computationally more efficient, particularly when resources like GPU memory are scarce.

关键词： Training computer vision Costs Neural networks Graphics processing units Organizations Approximation algorithms

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Few-shot Learning with Noisy Labels

Few-shot Learning with Noisy Labels

引用

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

作者： Liang, Kevin J. Rangrej, Samrudhdhi B. Petrovic, Vladan Hassner, Tal Facebook AI Res Menlo Pk CA 94025 USA McGill Univ Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh mislabeled versus correct samples. (3) Finally, we extensively test these methods on noisy versions of Mini-ImageNet and TieredImageNet. Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise.

关键词： Training computer vision Computational modeling Prototypes Transformers Robustness pattern recognition

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Learning to Learn across Diverse Data Biases in Deep Face recognition

Learning to Learn across Diverse Data Biases in Deep Face Re...

引用

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

作者： Liu, Chang Yu, Xiang Tsai, Yi-Hsuan Faraki, Masoud Moslemi, Ramin Chandraker, Manmohan Fu, Yun Northeastern Univ Boston MA 02115 USA NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data. However, the data used for training CNNs is often imbalanced. Prior works largely focus on the long-tailed nature of face datasets in data volume per identity, or focus on single bias variation. In this paper, we show that many bias variations such as ethnicity, head pose, occlusion and blur can jointly affect the accuracy significantly. We propose a sample level weighting approach termed Multi-variation Cosine Margin (MvCoM), to simultaneously consider the multiple variation factors, which orthogonally enhances the face recognition losses to incorporate the importance of training samples. Further, we leverage a learning to learn approach, guided by a held-out mew learning set and use an additive modeling to predict the MvCoM. Extensive experiments on challenging face recognition benchmarks demonstrate the advantages of our method in jointly handling imbalances due to multiple variations.

关键词： Training computer vision Additives Face recognition Computational modeling Predictive models Benchmark testing

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PubTables-1M: Towards comprehensive table extraction from unstructured documents

PubTables-1M: Towards comprehensive table extraction from un...

引用

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

作者： Smock, Brandon Pesala, Rohith Abraham, Robin Microsoft Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks. Data and code will be released at https://***/microsoft/table-transformer.

关键词： Training computer vision Object detection Machine learning Transformers Functional analysis pattern recognition

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Region-Aware Face Swapping

Region-Aware Face Swapping

引用

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

作者： Xu, Chao Zhang, Jiangning Hua, Miao He, Qian Yi, Zili Liu, Yong Zhejiang Univ APRIL Lab Hangzhou Peoples R China Bytedance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: 1) Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction. 2) Global Source Feature-Adaptive (SFA) branch further complements global identity-relevant cues for generating identity-consistent swapped faces. Besides, we propose a Face Mask Predictor (FMP) module incorporated with StyleGAN2 to predict identity-relevant soft facial masks in an unsupervised manner that is more practical for generating harmonious high-resolution faces. Abundant experiments qualitatively and quantitatively demonstrate the superiority of our method for generating more identity-consistent high-resolution swapped faces over SOTA methods, e.g., obtaining 96.70 ID retrieval that outperforms SOTA MegaFS by 5.87 up arrow.

关键词： computer vision Face recognition Semantics Transformers

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Generalizable Human Pose Triangulation

Generalizable Human Pose Triangulation

引用

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

作者： Bartol, Kristijan Bojanic, David Petkovic, Tomislav Pribanic, Tomislav Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We address the problem of generalizability for multi-view 3D human pose estimation. The standard approach is to first detect 2D keypoints in images and then apply triangulation from multiple views. Even though the existing methods achieve remarkably accurate 3D pose estimation on public benchmarks, most of them are limited to a single spatial camera arrangement and their number. Several methods address this limitation but demonstrate significantly degraded performance on novel views. We propose a stochastic framework for human pose triangulation and demonstrate a superior generalization across different camera arrangements on two public datasets. In addition, we apply the same approach to the fundamental matrix estimation problem, showing that the proposed method can successfully apply to other computer vision problems. The stochastic framework achieves more than 8.8% improvement on the 3D pose estimation task, compared to the state-of-the-art, and more than 30% improvement for fundamnental matrix estimation, compared to a standard algorithm.

关键词： Training computer vision Three-dimensional displays Computational modeling Pose estimation Stochastic processes Training data

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Multi-instance Point Cloud Registration by Efficient Correspondence Clustering

Multi-instance Point Cloud Registration by Efficient Corresp...

引用

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

作者： Tang, Weixuan Zou, Danping Shanghai Jiao Tong Univ Shanghai Key Lab Nav & Locat Based Serv Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outliers increase. We propose to directly group the set of noisy correspondences into different clusters based on a distance invariance matrix. The instances and outliers are automatically identified through clustering. Our method is robust and fast. We evaluated our method on both synthetic and real-world datasets. The results show that our approach can correctly register up to 20 instances with an F1 score of 90.46% in the presence of 70% outliers, which performs significantly better and at least 10x faster than existing methods. (Source code : https://***/SJTU-ViSYS/multi-instant-reg)

关键词： Point cloud compression Three-dimensional displays Robot vision systems Pose estimation Robustness Sensors pattern recognition

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Enabling Equivariance for Arbitrary Lie Groups

Enabling Equivariance for Arbitrary Lie Groups

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

作者： MacDonald, Lachlan E. Ramasinghe, Sameera Lucey, Simon Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time with more general geometric transformations of inputs. Recently, this limitation has motivated a shift in focus from CNNs to Capsule Networks (CapsNets). However, CapsNets suffer from admitting relatively few theoretical guarantees of invariance. We introduce a rigourous mathematical framework to permit invariance to any Lie group of warps, exclusively using convolutions (over Lie groups), without the need for capsules. Previous work on group convolutions has been hampered by strong assumptions about the group, which precludes the application of such techniques to common warps in computer vision such as affine and homographic. Our framework enables the implementation of group convolutions over any finite-dimensional Lie group. We empirically validate our approach on the benchmark affine-invariant classification task, where we achieve similar to 30% improvement in accuracy against conventional CNNs while outperforming most CapsNets. As further illustration of the generality of our framework, we train a homography-convolutional model which achieves superior robustness on a homography-perturbed dataset, where CapsNet results degrade.

关键词： Degradation computer vision Perturbation methods Benchmark testing Mathematical models Robustness pattern recognition

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