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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是251-260 订阅

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EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment

EBSR: Feature Enhanced Burst Super-Resolution with Deformabl...

引用

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

作者： Luo, Ziwei Yu, Lei Mo, Xuan Li, Youwei Jia, Lanpeng Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665448994

We propose a novel architecture to handle the problem of multi frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.

关键词： Training computer vision Correlation Tracking Convolution conferences Superresolution

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Generative Zero-shot Network Quantization

Generative Zero-shot Network Quantization

引用

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

作者： He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

关键词： Training Quantization (signal) Image resolution Image synthesis Training data pattern recognition Image restoration

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Domain Adaptable Normalization for Semi-Supervised Action recognition in the Dark

Domain Adaptable Normalization for Semi-Supervised Action Re...

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

作者： Liang, Zixi Chen, Jiajun Chen, Rui Zheng, Bingbing Zhou, Mingyue Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Action recognition in the dark is gaining more and more attention with the rapid development of intelligent recognition applications in real-world applications, e.g. self-driving at night and night surveillance. However, limited by the expensive labeling cost, it is impractical to produce a large-scale labeled dataset only for dark environments. Therefore, a practical solution adopted is to transfer models trained from clear environments to dark environments through semi-supervised learning. However, prior works rely heavily on additional efforts such as extra annotations, or extra sensors. To this end, we proposed a novel and simple Domain Adaptable Normalization (DANorm) method to align different domains directly, which consists of feature normalization, angle constraint and the Pseudo-Label. Specifically, the proposed DANorm method enables the model automatically learning the associated features between labeled source domain and unlabeled target domain by constraining the feature subspace vectors. Experimental results show that our model achieves superiority performance on Semi-supervised ARID dataset.

关键词： Adaptation models computer vision Costs Surveillance conferences Semisupervised learning pattern recognition

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Large Receptive Field Networks for High-Scale Image Super-Resolution 31

Large Receptive Field Networks for High-Scale Image Super-Re...

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

作者： Seif, George Androutsos, Dimitrios Ryerson Univ Toronto ON Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.

关键词： Kernel Image resolution Convolutional codes Convolution Signal resolution Memory management computer vision

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Towards efficient feature sharing in MIMO architectures

Towards efficient feature sharing in MIMO architectures

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

作者： Sun, Remy Rame, Alexandre Masson, Clement Thome, Nicolas Cord, Matthieu Sorbonne Univ MLIA ISIR Paris France Conservatoire Natl Arts & Metiers CEDRIC Vertigo Paris France Thales Land & Air Syst Elancourt France Valeo Ai Paris France

ISBN: (纸本)9781665487399

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR 100 show our adjustments allow feature sharing and improve model performance for small architectures.

关键词： Performance evaluation computer vision Impedance matching conferences Neural networks computer architecture pattern recognition

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Leveraging Unlabeled Data for Sketch-based Understanding

Leveraging Unlabeled Data for Sketch-based Understanding

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

作者： Morales, Javier Murrugarra-Llerena, Nils Saavedra, Jose M. Univ Chile Dept Comp Sci Santiago Chile Weber State Univ Dept Comp Sci Ogden UT 84408 USA Univ Los Andes Santiago Chile

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Sketch-based understanding is a critical component of human cognitive learning and is a primitive communication means between humans. This topic has recently attracted the interest of the computer vision community as sketching represents a powerful tool to express static objects and dynamic scenes. Unfortunately, despite its broad application domains, the current sketch-based models strongly rely on labels for supervised training, ignoring knowledge from unlabeled data, thus limiting the underlying generalization and the applicability. Therefore, we present a study about the use of unlabeled data to improve a sketch-based model. To this end, we evaluate variations of VAE and semi-supervised VAE, and present an extension of BYOL to deal with sketches. Our results show the superiority of sketch-BYOL, which outperforms other self-supervised approaches increasing the retrieval performance for known and unknown categories. Furthermore, we show how other tasks can benefit from our proposal.

关键词： Training computer vision TV Limiting Image edge detection Image retrieval Data models

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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

A Vision-based System for Traffic Anomaly Detection using De...

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

作者： Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA

ISBN: (纸本)9781665448994

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.

关键词： Deep learning Adaptation models Roads conferences Estimation Real-time systems pattern recognition

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Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Training Domain-invariant Object Detector Faster with Featur...

引用

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

作者： Lee, Chaehyeon Seo, Junghoon Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SI Analyt Co Ltd Daejeon South Korea SI Analyt Daejeon South Korea

ISBN: (纸本)9781665448994

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online(1).

关键词： Training computer vision Vehicle detection conferences Object detection Detectors Benchmark testing

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PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

PanoDR: Spherical Panorama Diminished Reality for Indoor Sce...

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

作者： Gkitsas, Vasileios Sterzentsenko, Vladimiros Zioulis, Nikolaos Albanis, Georgios Zarpalas, Dimitrios Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9781665448994

The rising availability of commercial 360 degrees cameras that democratize indoor scanning, has increased the interest for novel applications, such as interior space re-design. Diminished Reality (DR) fulfills the requirement of such applications, to remove existing objects in the scene, essentially translating this to a counterfactual inpainting task. While recent advances in data-driven inpainting have shown significant progress in generating realistic samples, they are not constrained to produce results with reality mapped structures. To preserve the 'reality' in indoor (re-)planning applications, the scene's structure preservation is crucial. To ensure structure-aware counterfactual inpainting, we propose a model that initially predicts the structure of a indoor scene and then uses it to guide the reconstruction of an empty - background only - representation of the same scene. We train and compare against other state-of-the-art methods on a version of the Structured3D dataset [47] modified for DR, showing superior results in both quantitative metrics and qualitative results, but more interestingly, our approach exhibits a much faster convergence rate. Code and models are available at ***/VCL3D/PanoDR/

关键词： Measurement Image segmentation computer vision conferences Predictive models Planning pattern recognition

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Dual-Branch Collaborative Transformer for Virtual Try-On

Dual-Branch Collaborative Transformer for Virtual Try-On

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

作者： Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.

关键词： Convolutional codes Industries Clothing Collaboration computer architecture Transformers pattern recognition

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