Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision最新文献_第8页

Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors 基于原型形状先验的半监督单视图三维重建

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-30 DOI: 10.48550/arXiv.2209.15383

Zhen Xing, Hengduo Li, Zuxuan Wu, Yu-Gang Jiang

{"title":"Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors","authors":"Zhen Xing, Hengduo Li, Zuxuan Wu, Yu-Gang Jiang","doi":"10.48550/arXiv.2209.15383","DOIUrl":"https://doi.org/10.48550/arXiv.2209.15383","url":null,"abstract":"The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspired by the recent success of semi-supervised image classification tasks, we propose SSP3D, a semi-supervised framework for 3D reconstruction. In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction. We further introduce a discriminator-guided module to incentivize better shape generation, as well as a regularizer to tolerate noisy training samples. On the ShapeNet benchmark, the proposed approach outperforms previous supervised methods by clear margins under various labeling ratios, (i.e., 1%, 5% , 10% and 20%). Moreover, our approach also performs well when transferring to real-world Pix3D datasets under labeling ratios of 10%. We also demonstrate our method could transfer to novel categories with few novel supervised data. Experiments on the popular ShapeNet dataset show that our method outperforms the zero-shot baseline by over 12% and we also perform rigorous ablations and analysis to validate our approach.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"462 1","pages":"535-551"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76414818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition 教学在哪里看:低分辨率人脸识别的注意力相似知识蒸馏

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-29 DOI: 10.48550/arXiv.2209.14498

Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee

{"title":"Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition","authors":"Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee","doi":"10.48550/arXiv.2209.14498","DOIUrl":"https://doi.org/10.48550/arXiv.2209.14498","url":null,"abstract":"Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention similarity knowledge distillation approach, which transfers attention maps obtained from a high resolution (HR) network as a teacher into an LR network as a student to boost LR recognition performance. Inspired by humans being able to approximate an object's region from an LR image based on prior knowledge obtained from HR images, we designed the knowledge distillation loss using the cosine similarity to make the student network's attention resemble the teacher network's attention. Experiments on various LR face related benchmarks confirmed the proposed method generally improved recognition performances on LR settings, outperforming state-of-the-art results by simply transferring well-constructed attention maps. The code and pretrained models are publicly available in the https://github.com/gist-ailab/teaching-where-to-look.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"123 1","pages":"631-647"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88600848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Understanding Collapse in Non-contrastive Siamese Representation Learning 非对比暹罗表征学习中的崩溃理解

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-29 DOI: 10.1007/978-3-031-19821-2_28

Alexander C. Li, Alexei A. Efros, Deepak Pathak

引用次数: 15

DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image DELTAR:基于轻量级ToF传感器和RGB图像的深度估计

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-27 DOI: 10.48550/arXiv.2209.13362

Yijin Li, Xinyang Liu, Wenqian Dong, Han Zhou, H. Bao, Guofeng Zhang, Yinda Zhang, Zhaopeng Cui

{"title":"DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image","authors":"Yijin Li, Xinyang Liu, Wenqian Dong, Han Zhou, H. Bao, Guofeng Zhang, Yinda Zhang, Zhaopeng Cui","doi":"10.48550/arXiv.2209.13362","DOIUrl":"https://doi.org/10.48550/arXiv.2209.13362","url":null,"abstract":"Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection, etc. However, due to their specific measurements (depth distribution in a region instead of the depth value at a certain pixel) and extremely low resolution, they are insufficient for applications requiring high-fidelity depth such as 3D reconstruction. In this paper, we propose DELTAR, a novel method to empower light-weight ToF sensors with the capability of measuring high resolution and accurate depth by cooperating with a color image. As the core of DELTAR, a feature extractor customized for depth distribution and an attention-based neural architecture is proposed to fuse the information from the color and ToF domain efficiently. To evaluate our system in real-world scenarios, we design a data collection device and propose a new approach to calibrate the RGB camera and ToF sensor. Experiments show that our method produces more accurate depth than existing frameworks designed for depth completion and depth super-resolution and achieves on par performance with a commodity-level RGB-D sensor. Code and data are available at https://zju3dv.github.io/deltar/.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"15 1","pages":"619-636"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83874951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection AcroFOD:一种跨域小镜头目标检测的自适应方法

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.10904

Yipeng Gao, Lingxiao Yang, Yunmu Huang, Song Xie, Shiyong Li, Weihao Zheng

{"title":"AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection","authors":"Yipeng Gao, Lingxiao Yang, Yunmu Huang, Song Xie, Shiyong Li, Weihao Zheng","doi":"10.48550/arXiv.2209.10904","DOIUrl":"https://doi.org/10.48550/arXiv.2209.10904","url":null,"abstract":"Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target domain data; (2) Potential over-adaptation and misleading caused by inappropriately amplified target samples without any restriction. To address these challenges, we propose an adaptive method consisting of two parts. First, we propose an adaptive optimization strategy to select augmented data similar to target samples rather than blindly increasing the amount. Specifically, we filter the augmented candidates which significantly deviate from the target feature distribution in the very beginning. Second, to further relieve the data limitation, we propose the multi-level domain-aware data augmentation to increase the diversity and rationality of augmented data, which exploits the cross-image foreground-background mixture. Experiments show that the proposed method achieves state-of-the-art performance on multiple benchmarks.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"285 1","pages":"673-690"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77736103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion 用于RGB图像融合的深度分层变分自编码器

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.11277

Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche, G. Neumann

{"title":"FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion","authors":"Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche, G. Neumann","doi":"10.48550/arXiv.2209.11277","DOIUrl":"https://doi.org/10.48550/arXiv.2209.11277","url":null,"abstract":". Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior knowledge nor find regu-larities in a given dataset or they are restricted to a single application. We overcome this shortcoming by presenting a novel deep hierarchical variational autoencoder called FusionVAE that can serve as a basis for many fusion tasks. Our approach is able to generate diverse image samples that are conditioned on multiple noisy, occluded, or only partially visible input images. We derive and optimize a variational lower bound for the conditional log-likelihood of FusionVAE. In order to assess the fusion capabilities of our model thoroughly, we created three novel datasets for image fusion based on popular computer vision datasets. In our experiments, we show that FusionVAE learns a representation of aggregated information that is relevant to fusion tasks. The results demonstrate that our approach outperforms traditional methods significantly. Furthermore, we present the advantages and disadvantages of different design choices.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"36 1","pages":"674-691"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86771546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IntereStyle: Encoding an Interest Region for Robust StyleGAN Inversion 为稳健StyleGAN反演编码兴趣区域

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.10811

S. Moon, GyeongMoon Park

{"title":"IntereStyle: Encoding an Interest Region for Robust StyleGAN Inversion","authors":"S. Moon, GyeongMoon Park","doi":"10.48550/arXiv.2209.10811","DOIUrl":"https://doi.org/10.48550/arXiv.2209.10811","url":null,"abstract":"Recently, manipulation of real-world images has been highly elaborated along with the development of Generative Adversarial Networks (GANs) and corresponding encoders, which embed real-world images into the latent space. However, designing encoders of GAN still remains a challenging task due to the trade-off between distortion and perception. In this paper, we point out that the existing encoders try to lower the distortion not only on the interest region, e.g., human facial region but also on the uninterest region, e.g., background patterns and obstacles. However, most uninterest regions in real-world images are located at out-of-distribution (OOD), which are infeasible to be ideally reconstructed by generative models. Moreover, we empirically find that the uninterest region overlapped with the interest region can mangle the original feature of the interest region, e.g., a microphone overlapped with a facial region is inverted into the white beard. As a result, lowering the distortion of the whole image while maintaining the perceptual quality is very challenging. To overcome this trade-off, we propose a simple yet effective encoder training scheme, coined IntereStyle, which facilitates encoding by focusing on the interest region. IntereStyle steers the encoder to disentangle the encodings of the interest and uninterest regions. To this end, we filter the information of the uninterest region iteratively to regulate the negative impact of the uninterest region. We demonstrate that IntereStyle achieves both lower distortion and higher perceptual quality compared to the existing state-of-the-art encoders. Especially, our model robustly conserves features of the original images, which shows the robust image editing and style mixing results. We will release our code with the pre-trained model after the review.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"22 1","pages":"460-476"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83881888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

PREF: Predictability Regularized Neural Motion Fields PREF:可预测性正则化神经运动场

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-21 DOI: 10.48550/arXiv.2209.10691

Liangchen Song, Xuan Gong, Benjamin Planche, Meng Zheng, D. Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu

{"title":"PREF: Predictability Regularized Neural Motion Fields","authors":"Liangchen Song, Xuan Gong, Benjamin Planche, Meng Zheng, D. Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu","doi":"10.48550/arXiv.2209.10691","DOIUrl":"https://doi.org/10.48550/arXiv.2209.10691","url":null,"abstract":"Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"99 1","pages":"664-681"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74675657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

NashAE: Disentangling Representations through Adversarial Covariance Minimization 通过对抗性协方差最小化来解纠缠表征

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-21 DOI: 10.48550/arXiv.2209.10677

Eric C. Yeats, Frank Liu, David A. P. Womble, Hai Li

{"title":"NashAE: Disentangling Representations through Adversarial Covariance Minimization","authors":"Eric C. Yeats, Frank Liu, David A. P. Womble, Hai Li","doi":"10.48550/arXiv.2209.10677","DOIUrl":"https://doi.org/10.48550/arXiv.2209.10677","url":null,"abstract":"We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no assumptions on the number or distribution of the individual latent variables to be extracted). In this method which we call NashAE, high-dimensional feature disentanglement is accomplished in the low-dimensional latent space of a standard autoencoder (AE) by promoting the discrepancy between each encoding element and information of the element recovered from all other encoding elements. Disentanglement is promoted efficiently by framing this as a minmax game between the AE and an ensemble of regression networks which each provide an estimate of an element conditioned on an observation of all other elements. We quantitatively compare our approach with leading disentanglement methods using existing disentanglement metrics. Furthermore, we show that NashAE has increased reliability and increased capacity to capture salient data characteristics in the learned latent representation.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"13 1","pages":"36-51"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85665731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution KXNet:一个模型驱动的盲超分辨率深度神经网络

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-09-21 DOI: 10.48550/arXiv.2209.10305

J. Fu, Hong Wang, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu

{"title":"KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution","authors":"J. Fu, Hong Wang, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu","doi":"10.48550/arXiv.2209.10305","DOIUrl":"https://doi.org/10.48550/arXiv.2209.10305","url":null,"abstract":"Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"61 1","pages":"235-253"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86085056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10