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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221-230 订阅

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Representative Batch Normalization with Feature Calibration

Representative Batch Normalization with Feature Calibration

引用

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

作者： Gao, Shang-Hua Han, Qi Li, Duo Cheng, Ming-Ming Peng, Pai Nankai Univ CS TKLNDST Tianjin Peoples R China HKUST Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation.

关键词： Training computer vision Costs Neural networks Standardization Calibration pattern recognition

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Cross-View Regularization for Domain Adaptive Panoptic Segmentation

Cross-View Regularization for Domain Adaptive Panoptic Segme...

引用

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

作者： Huang, Jiaxing Guan, Dayan Xiao, Aoran Lu, Shijian Nanyang Technol Univ Sch Comp Sci Engn Singapore Singapore

ISBN: (纸本)9781665445092

Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. However, most existing research was conducted under a supervised learning setup whereas unsupervised domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency lever-ages semantic invariance across the same image of the different styles which 'fabricates' certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g. synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art.

关键词： Image segmentation computer vision Adaptive systems Semantics Supervised learning pattern recognition Task analysis

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Truly shift-invariant convolutional neural networks

Truly shift-invariant convolutional neural networks

引用

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

作者： Chaman, Anadi Dokmanic, Ivan Univ Illinois Champaign IL 61820 USA Univ Basel Basel Switzerland

ISBN: (纸本)9781665445092

Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input-a problem caused by the presence of downsampling (stride) layers. The existing solutions rely either on data augmentation or on anti-aliasing, both of which have limitations and neither of which enables perfect shift invariance. Additionally, the gains obtained from these methods do not extend to image patterns not seen during training. To address these challenges, we propose adaptive polyphase sampling (APS), a simple sub-sampling scheme that allows convolutional neural networks to achieve 100% consistency in classification performance under shifts, without any loss in accuracy. With APS, the networks exhibit perfect consistency to shifts even before training, making it the first approach that makes convolutional neural networks truly shift-invariant.

关键词： Training computer vision Adaptive systems Convolution pattern recognition Convolutional neural networks

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Energy-Based Learning for Scene Graph Generation

Energy-Based Learning for Scene Graph Generation

引用

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

作者： Suhail, Mohammed Mittal, Abhay Siddiquie, Behjat Broaddus, Chris Eledath, Jayan Medioni, Gerard Sigal, Leonid Univ British Columbia Vancouver BC Canada Vector Inst AI Bengaluru India Canada CIFAR AI Chair Montreal PQ Canada Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework + to train existing state-of-the-art models and obtain a significant performance improvement, of up to 21% and 27%, on the Visual Genome [9] and GQA [5] benchmark datasets, respectively. Furthermore, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce.

关键词： Training Visualization computer vision Computational modeling Genomics Benchmark testing pattern recognition

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

引用

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

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Explore Image Deblurring via Encoded Blur Kernel Space

Explore Image Deblurring via Encoded Blur Kernel Space

引用

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

作者： Phong Tran Anh Tuan Tran Quynh Phung Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method. The code is available at https://***/VinAIResearch/blur-kernelspace-exploring.

关键词： Deep learning computer vision Codes Design methodology Gray-scale Image restoration pattern recognition

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Transformation Invariant Few-Shot Object Detection

Transformation Invariant Few-Shot Object Detection

引用

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

作者： Li, Aoxue Li, Zhenguo Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Few-shot object detection (FSOD) aims to learn detectors that can be generalized to novel classes with only a few instances. Unlike previous attempts that exploit meta-learning techniques to facilitate FSOD, this work tackles the problem from the perspective of sample expansion. To this end, we propose a simple yet effective Transformation Invariant Principle (TIP) that can be flexibly applied to various meta-learning models for boosting the detection performance on novel class objects. Specifically, by introducing consistency regularization on predictions from various transformed images, we augment vanilla FSOD models with the generalization ability to objects perturbed by various transformation, such as occlusion and noise. Importantly, our approach can extend supervised FSOD models to naturally cope with unlabeled data, thus addressing a more practical and challenging semi-supervised FSOD problem. Extensive experiments on PASCAL VOC and MSCOCO datasets demonstrate the effectiveness of our TIP under both of the two FSOD settings.

关键词： computer vision Object detection Detectors Predictive models Boosting Data models pattern recognition

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The Translucent Patch: A Physical and Universal Attack on Object Detectors

The Translucent Patch: A Physical and Universal Attack on Ob...

引用

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

作者： Zolfi, Alon Kravchik, Moshe Elovici, Yuval Shabtai, Asaf Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

ISBN: (纸本)9781665445092

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera's lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes.

关键词： computer vision Face recognition Optimization methods Detectors Object detection Cameras Autonomous vehicles

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SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

SetVAE: Learning Hierarchical Composition for Generative Mod...

引用

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

作者： Kim, Jinwoo Yoo, Jaehoon Lee, Juho Hong, Seunghoon Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision.

关键词： computer vision Art Data models Cognition Encoding pattern recognition Task analysis

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Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

Bilevel Online Adaptation for Out-of-Domain Human Mesh Recon...

引用

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

作者： Guan, Shanyan Xu, Jingwei Wang, Yunbo Ni, Bingbing Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper considers a new problem of adapting a pretrained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model [36] underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks(1).

关键词： Training Adaptation models computer vision Benchmark testing Reconstruction algorithms Cameras pattern recognition

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