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Fast and Accurate Model Scaling

Fast and Accurate Model Scaling

引用

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

作者： Dollar, Piotr Singh, Mannat Girshick, Ross Facebook AI Res FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

In this work we analyze strategies for convolutional neural network scaling;that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. While various scaling strategies exist, their tradeoffs are not fully understood. Existing analysis typically focuses on the interplay of accuracy and flops (floating point operations). Yet, as we demonstrate, various scaling strategies affect model parameters, activations, and consequently actual runtime quite differently. In our experiments we show the surprising result that numerous scaling strategies yield networks with similar accuracy but with widely varying properties. This leads us to propose a simple fast compound scaling strategy that encourages primarily scaling model width, while scaling depth and resolution to a lesser extent. Unlike currently popular scaling strategies, which result in about O(s) increase in model activation w.r.t. scaling flops by a factor of s, the proposed fast compound scaling results in close to O(root s) increase in activations, while achieving excellent accuracy. Fewer activations leads to speedups on modern memory-bandwidth limited hardware (e.g., GPUs). More generally, we hope this work provides a framework for analyzing scaling strategies under various computational constraints.

关键词： computer vision Runtime Computational modeling Hardware pattern recognition Compounds Convolutional neural networks

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Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

Temporally-Weighted Hierarchical Clustering for Unsupervised...

引用

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

作者： Sarfraz, M. Saquib Murray, Naila Sharma, Vivek Diba, Ali van Gool, Luc Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Facebook AI Res Menlo Pk CA USA MIT Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA Katholieke Univ Leuven Leuven Belgium Swiss Fed Inst Technol Zurich Switzerland Daimler TSS Ulm Germany

ISBN: (纸本)9781665445092

Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed frame-level annotations. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video. Our main finding is that representing a video with a 1-nearest neighbor graph by taking into account the time progression is sufficient to form semantically and temporally consistent clusters of frames where each cluster may represent some action in the video. Additionally, we establish strong unsupervised baselines for action segmentation and show significant performance improvements over published unsupervised methods on five challenging action segmentation datasets. Our code is available.

关键词： Training Measurement Visualization computer vision Codes Spatiotemporal phenomena pattern recognition

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NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

NBNet: Noise Basis Learning for Image Denoising with Subspac...

引用

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

作者： Cheng, Shen Wang, Yuzhi Huang, Haibin Liu, Donghao Fan, Haoqiang Liu, Shuaicheng Megvii Technol Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reduction by image-adaptive projection. Specifically, we propose to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space. Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space. Our key insight is that projection can naturally maintain the local structure of input signal, especially for areas with low light or weak textures. Towards this end, we propose SSA, a non-local attention module we design to explicitly learn the basis generation as well as subspace projection. We further incorporate SSA with NBNet, a UNet structured network designed for end-to-end image denosing based. We conduct evaluations on benchmarks, including SIDD and DND, and NBNet achieves state-of-the-art performance on PSNR and SSIM with significantly less computational cost.

关键词： computer vision Convolution Computational modeling Noise reduction Network architecture pattern recognition Computational efficiency

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Learning To Count Everything

Learning To Count Everything

引用

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

作者： Ranjan, Viresh Sharma, Udbhav Thu Nguyen Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781665445092

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://***/cvlab-stonybrook/LearningToCountEverything.

关键词： Visualization computer vision Codes Annotations Animals Detectors pattern recognition

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Learning Progressive Point Embeddings for 3D Point Cloud Generation

Learning Progressive Point Embeddings for 3D Point Cloud Gen...

引用

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

作者： Wen, Cheng Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci 6 Cleveland St Darlington NSW 2008 Australia

ISBN: (纸本)9781665445092

Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clouds, because of the difficulties in 1) learning effective pointwise representations;and 2) generating realistic point clouds from complex distributions. In this paper, we devise a dual-generators framework for point cloud generation, which generalizes vanilla generative adversarial learning framework in a progressive manner. Specifically, the first generator aims to learn effective point embeddings in a breadth-first manner, while the second generator is used to refine the generated point cloud based on a depth-first point embedding to generate a robust and uniform point cloud. The proposed dual-generators framework thus is able to progressively learn effective point embeddings for accurate point cloud generation. Experimental results on a variety of object categories from the most popular point cloud generation dataset, ShapeNet, demonstrate the state-of-the-art performance of the proposed method for accurate point cloud generation.

关键词： Deep learning Solid modeling computer vision Three-dimensional displays Generators Adversarial machine learning pattern recognition

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Sparse Multi-Path Corrections in Fringe Projection Profilometry

Sparse Multi-Path Corrections in Fringe Projection Profilome...

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

作者： Zhang, Yu Lau, Daniel Wipf, David Nanjing Univ Nanjing Peoples R China Univ Kentucky Lexington KY 40506 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.

关键词： Linear systems Surface reconstruction computer vision Lighting Cameras Mathematical models pattern recognition

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Double low-rank representation with projection distance penalty for clustering

Double low-rank representation with projection distance pena...

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

作者： Fu, Zhiqiang Zhao, Yao Chang, Dongxia Zhang, Xingxing Wang, Yiming Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structure. Besides the global low-rank constraint, the local geometrical structure is additionally exploited via a projection distance penalty in our DLRRPD, thus facilitating a more favorable graph. Moreover, to improve the robustness of DLRRPD to noises, we introduce a Laplacian rank constraint, which can further encourage the learned graph to be more discriminative for clustering tasks. Meanwhile, Frobenius norm (instead of the popularly used nuclear norm) is employed to enforce the graph to be more block-diagonal with lower complexity. Extensive experiments have been conducted on synthetic, real, and noisy data to show that the proposed method outperforms currently available alternatives by a margin of 1.0%similar to 10.1%.

关键词： computer vision Laplace equations Robustness pattern recognition Complexity theory Noise measurement Task analysis

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Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods

Enhance Curvature Information by Structured Stochastic Quasi...

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

作者： Yang, Minghan Xu, Dong Chen, Hongyu Wen, Zaiwen Chen, Mengyun Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods.

关键词： Training computer vision pattern recognition Convolutional neural networks Artificial intelligence Portfolios Optimization

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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Stochastic Whitening Batch Normalization

Stochastic Whitening Batch Normalization

引用

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

作者： Zhang, Shengdong Nezhadarya, Ehsan Fashandi, Homa Liu, Jiayi Graham, Darin Shah, Mohak LG Elect Canada Toronto AI Lab Toronto ON Canada LG Elect USA Amer R&D Lab Santa Clara CA USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton's method. However, since Newton's method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks.

关键词： Training Deep learning computer vision Stochastic processes pattern recognition Iterative methods Convolutional neural networks

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