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共 11423 条记录，以下是191-200 订阅

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Pulsar: Efficient Sphere-based Neural Rendering

Pulsar: Efficient Sphere-based Neural Rendering

引用

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

作者： Lassner, Christoph Zollhofer, Michael Facebook Real Labs Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering.

关键词： Training Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Distance measurement Topology

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ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

ArtFlow: Unbiased Image Style Transfer via Reversible Neural...

引用

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

作者： An, Jie Huang, Siyu Song, Yibing Dou, Dejing Liu, Wei Luo, Jiebo Univ Rochester Rochester NY 14627 USA Baidu Res Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this paper, we propose ArtFlow to prevent content leak during universal style transfer. ArtFlow consists of reversible neural flows and an unbiased feature transfer module. It supports both forward and backward inferences and operates in a projection-transfer-reversion scheme. The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way. Extensive experiments demonstrate that ArtFlow achieves comparable performance to state-of-the-art style transfer methods while avoiding content leak.

关键词： computer vision Neural networks Feature extraction Inference algorithms pattern recognition

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Fully Convolutional Networks for Panoptic Segmentation

Fully Convolutional Networks for Panoptic Segmentation

引用

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

作者： Li, Yanwei Zhao, Hengshuang Qi, Xiaojuan Wang, Liwei Li, Zeming Sun, Jian Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Oxford Oxford England Univ Hong Kong Hong Kong Peoples R China MEGVII Technol Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline. In particular, Panoptic FCN encodes each object instance or stuff category into a specific kernel weight with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly. With this approach, instance-aware and semantically consistent prosperties for things and stuff can be respectively satisfied in a simple generate-kernel-then-segment workflow. Without extra boxes for localization or instance separation, the proposed approach outperforms previous box-based and -free models with high efficiency on COCO, Cityscapes, and Mapillary Vistas datasets with single scale input.

关键词： Convolutional codes Location awareness computer vision Semantics Pipelines Generators pattern recognition

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

引用

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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Natural Adversarial Examples

Natural Adversarial Examples

引用

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

作者： Hendrycks, Dan Zhao, Kevin Basart, Steven Steinhardt, Jacob Song, Dawn Univ Calif Berkeley Berkeley CA 94720 USA Univ Washington Seattle WA 98195 USA UChicago Chicago IL USA

ISBN: (纸本)9781665445092

We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET- A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET- O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models.

关键词： Training computer vision Filtration Convolution Computational modeling Machine learning computer architecture

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Representation Learning via Global Temporal Alignment and Cycle-Consistency

Representation Learning via Global Temporal Alignment and Cy...

引用

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

作者： Hadji, Isma Derpanis, Konstantinos G. Jepson, Allan D. Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle-consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.

关键词： computer vision Three-dimensional displays Probabilistic logic Routing pattern recognition Synchronization Task analysis

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D²IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

D<SUP>2</SUP>IM-Net: Learning Detail Disentangled Implicit F...

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

作者： Li, Manyi Zhang, Hao Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined (DIM)-I-2-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

关键词： Surface reconstruction computer vision Three-dimensional displays Laplace equations Shape Decoding pattern recognition

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Capsule Network is Not More Robust than Convolutional Network

Capsule Network is Not More Robust than Convolutional Networ...

引用

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

作者： Gu, Jindong Tresp, Volker Hu, Han Univ Munich Munich Germany Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet affect its robustness. In this paper, we first carefully examine the special designs in CapsNet that differ from that of a ConvNet commonly used for image classification. The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization. Along with these major differences, we conduct comprehensive ablation studies on three kinds of robustness, including affine transformation, overlapping digits, and semantic representation. The study reveals that some designs, which are thought critical to CapsNet, actually can harm its robustness, i.e., the dynamic routing layer and the transformation process, while others are beneficial for the robustness. Based on these findings, we propose enhanced ConvNets simply by introducing the essential components behind the CapsNet's success. The proposed simple ConvNets can achieve better robustness than the CapsNet.

关键词： computer vision Aggregates Semantics Routing Robustness pattern recognition Task analysis

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AdaBins: Depth Estimation Using Adaptive Bins

AdaBins: Depth Estimation Using Adaptive Bins

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

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

关键词： Measurement Image segmentation Three-dimensional displays Image resolution Estimation computer architecture Transformers

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KeepAugment: A Simple Information-Preserving Data Augmentation Approach

KeepAugment: A Simple Information-Preserving Data Augmentati...

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

作者： Gong, Chengyue Wang, Dilin Li, Meng Chandra, Vikas Liu, Qiang Univ Texas Austin Austin TX 78712 USA Facebook Mountain View CA USA

ISBN: (纸本)9781665445092

Data augmentation (DA) is an essential technique for training state-of-the-art deep learning systems. In this paper, we empirically show that the standard data augmentation methods may introduce distribution shift and consequently hurt the performance on unaugmented data during inference. To alleviate this issue, we propose a simple yet effective approach, dubbed KeepAugment, to increase the fidelity of augmented images. The idea is to use the saliency map to detect important regions on the original images and preserve these informative regions during augmentation. This information-preserving strategy allows us to generate more faithful training examples. Empirically, we demonstrate that our method significantly improves upon a number of prior art data augmentation schemes, e.g. AutoAugment, Cutout, random erasing, achieving promising results on image classification, semi-supervised image classification, multi-view multi-camera tracking and object detection.

关键词： Training Deep learning computer vision Art Object detection Boosting pattern recognition

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