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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111-120 订阅

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Learning optical flow from still images

Learning optical flow from still images

引用

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

作者： Aleotti, Filippo Poggi, Matteo Mattoccia, Stefano Univ Bologna Bologna Italy

ISBN: (纸本)9781665445092

This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. Specifically, we introduce a framework to generate accurate ground-truth optical flow annotations quickly and in large amounts from any readily available single real picture. Given an image, we use an off-the-shelf monocular depth estimation network to build a plausible point cloud for the observed scene. Then, we virtually move the camera in the reconstructed environment with known motion vectors and rotation angles, allowing us to synthesize both a novel view and the corresponding optical flow field connecting each pixel in the input image to the one in the new frame. When trained with our data, state-of-the-art optical flow networks achieve superior generalization to unseen real data compared to the same models trained either on annotated synthetic datasets or unlabeled videos, and better specialization if combined with synthetic images.

关键词： Training computer vision Computational modeling Training data Estimation Data models pattern recognition

来源：评论

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PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting

PhySG: Inverse Rendering with Spherical Gaussians for Physic...

引用

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

作者： Zhang, Kai Luan, Fujun Wang, Qianqian Bala, Kavita Snavely, Noah Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

We present PhySG, an end-to-end inverse rendering pipeline that includes a filly differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.

关键词： Geometry Reflectivity Pipelines Fitting Lighting Rendering (computer graphics) Mathematical models

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Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

Exponential Moving Average Normalization for Self-supervised...

引用

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

作者： Cai, Zhaowei Ravichandran, Avinash Maji, Subhransu Fowlkes, Charless Tu, Zhuowen Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781665445092

We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques. Unlike the standard BN, where the statistics are computed within each batch, EMAN, used in the teacher, updates its statistics by exponential moving average from the BN statistics of the student. This design reduces the intrinsic cross-sample dependency of BN and enhances the generalization of the teacher. EMAN improves strong baselines for self-supervised learning by 4-6/1-2 points and semi-supervised learning by about 7/2 points, when 1%/10% supervised labels are available on ImageNet. These improvements are consistent across methods, network architectures, training duration, and datasets, demonstrating the general effectiveness of this technique. The code will be made available online.

关键词： Training computer vision Codes Semisupervised learning Network architecture pattern recognition Standards

来源：评论

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Adaptive Convolutions for Structure-Aware Style Transfer

Adaptive Convolutions for Structure-Aware Style Transfer

引用

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

作者： Chandran, Prashanth Zoss, Gaspard Gotardo, Paulo Gross, Markus Bradley, Derek DisneyRes Studios Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665445092

Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However AdaIN is a global operation;thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted.

关键词： computer vision Image synthesis Real-time systems Weaving pattern recognition Decoding Task analysis

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CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo: Content-Style Modulation for Image Retrieval with Tex...

引用

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

作者： Lee, Seungmin Kim, Dongwan Han, Bohyung Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

We tackle the task of image retrieval with text feedback, where a reference image and modifier text are combined to identify the desired target image. We focus on designing an image-text compositor, i.e., integrating multi-modal inputs to produce a representation similar to that of the target image. In our algorithm, Content-Style Modulation (CoSMo), we approach this challenge by introducing two modules based on deep neural networks: the content and style modulators. The content modulator performs local updates to the reference image feature after normalizing the style of the image, where a disentangled multi-modal non-local block is employed to achieve the desired content modifications. Then, the style modulator reintroduces global style information to the updated feature. We provide an in-depth view of our algorithm and its design choices, and show that it accomplishes outstanding performance on multiple image-text retrieval benchmarks.

关键词： Deep learning computer vision Codes Image retrieval Modulation Benchmark testing pattern recognition

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Architectural Adversarial Robustness: The Case for Deep Pursuit

Architectural Adversarial Robustness: The Case for Deep Purs...

引用

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

作者： Cazenavette, George Murdock, Calvin Lucey, Simon Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by refraining each layer of a feed forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deepen real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.

关键词： Deep learning Resistance computer vision Sensitivity computer architecture Sparse representation Robustness

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Generalized Domain Adaptation

Generalized Domain Adaptation

引用

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

作者： Mitsuzumi, Yu Irie, Go Ikami, Daiki Shibata, Takashi NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781665445092

Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a general representation of UDA problems, named Generalized Domain Adaptation (GDA). GDA covers the major variants as special cases, which allows us to organize them in a comprehensive framework. Moreover, this generalization leads to a new challenging setting where existing methods fail, such as when domain labels are unknown, and class labels are only partially given to each domain. We propose a novel approach to the new setting. The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels. Extensive experiments using three benchmark datasets demonstrate that our method outperforms the state-of-the-art UDA methods in the new setting and that it is competitive in existing UDA variations as well.

关键词： computer vision Benchmark testing pattern recognition

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Understanding and Simplifying Perceptual Distances

Understanding and Simplifying Perceptual Distances

引用

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

作者： Amir, Dan Weiss, Yair Hebrew Univ Jerusalem Jerusalem Israel

ISBN: (纸本)9781665445092

Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classical losses such as L1 or L2 in pixel space. The source of this success remains somewhat mysterious, especially since a good loss does not require a particular CNN architecture nor a particular training method. In this paper we show that similar success can be achieved even with losses based on features of a deep CNN with random filters. We use the tool of infinite CNNs to derive an analytical form for perceptual similarity in such CNNs, and prove that the perceptual distance between two images is equivalent to the maximum mean discrepancy (MMD) distance between local distributions of small patches in the two images. We use this equivalence to propose a simple metric for comparing two images which directly computes the MMD between local distributions of patches in the two images. Our proposed metric is simple to understand, requires no deep networks, and gives comparable performance to perceptual metrics in a range of computer vision tasks.

关键词： Training computer vision computer architecture Tools Extraterrestrial measurements pattern recognition Convolutional neural networks

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FrameExit: Conditional Early Exiting for Efficient Video recognition

FrameExit: Conditional Early Exiting for Efficient Video Rec...

引用

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

作者： Ghodrati, Amir Bejnordi, Babak Ehteshami Habibian, Amirhossein Qualcomm AI Res Nijmegen Netherlands

ISBN: (纸本)9781665445092

In this paper, we propose a conditional early exiting framework for efficient video recognition. While existing works focus on selecting a subset of salient frames to reduce the computation costs, we propose to use a simple sampling strategy combined with conditional early exiting to enable efficient recognition. Our model automatically learns to process fewer frames for simpler videos and more frames for complex ones. To achieve this, we employ a cascade of gating modules to automatically determine the earliest point in processing where an inference is sufficiently reliable. We generate on-the-fly supervision signals to the gates to provide a dynamic trade-off between accuracy and computational cost. Our proposed model outperforms competing methods on three large-scale video benchmarks. In particular, on ActivityNet1.3 and mini-kinetics, we outperform the state-of-the-art efficient video recognition methods with 1.3x and 2.1x less GFLOPs, respectively. Additionally, our method sets a new state of the art for efficient video understanding on the HVU benchmark.

关键词： computer vision Costs Computational modeling Logic gates Benchmark testing Network architecture pattern recognition

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Invertible Image Signal Processing

Invertible Image Signal Processing

引用

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

作者： Xing, Yazhou Qian, Zian Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods.

关键词： computer vision Image coding Pipelines Transform coding Signal processing Rendering (computer graphics) Stability analysis

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