2021 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献

CrackFormer: Transformer Network for Fine-Grained Crack Detection 用于细粒度裂纹检测的变压器网络

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00376

Huajun Liu, Xiangyu Miao, C. Mertz, Chengzhong Xu, Hui Kong

{"title":"CrackFormer: Transformer Network for Fine-Grained Crack Detection","authors":"Huajun Liu, Xiangyu Miao, C. Mertz, Chengzhong Xu, Hui Kong","doi":"10.1109/ICCV48922.2021.00376","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.00376","url":null,"abstract":"Cracks are irregular line structures that are of interest in many computer vision applications. Crack detection (e.g., from pavement images) is a challenging task due to intensity in-homogeneity, topology complexity, low contrast and noisy background. The overall crack detection accuracy can be significantly affected by the detection performance on fine-grained cracks. In this work, we propose a Crack Transformer network (CrackFormer) for fine-grained crack detection. The CrackFormer is composed of novel attention modules in a SegNet-like encoder-decoder architecture. Specifically, it consists of novel self-attention modules with 1x1 convolutional kernels for efficient contextual information extraction across feature-channels, and efficient positional embedding to capture large receptive field contextual information for long range interactions. It also introduces new scaling-attention modules to combine outputs from the corresponding encoder and decoder blocks to suppress non-semantic features and sharpen semantic ones. The CrackFormer is trained and evaluated on three classical crack datasets. The experimental results show that the CrackFormer achieves the Optimal Dataset Scale (ODS) values of 0.871, 0.877 and 0.881, respectively, on the three datasets and outperforms the state-of-the-art methods.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"5 1","pages":"3763-3772"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74316604","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: 35

Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning 基于深度高斯迁移学习的深度神经网络快速高效部署

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00533

Qi Sun, Chen Bai, Tinghuan Chen, Hao Geng, Xinyun Zhang, Yang Bai, Bei Yu

引用次数: 8

Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements 从稀疏干涉测量推断黑洞流体动力学

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/iccv48922.2021.00234

Aviad Levis, Daeyoung Lee, J. Tropp, C. Gammie, K. Bouman

引用次数: 5

Graspness Discovery in Clutters for Fast and Accurate Grasp Detection 杂波中的抓握发现，实现快速准确的抓握检测

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01566

Chenxi Wang, Haoshu Fang, Minghao Gou, Hongjie Fang, Jin Gao, Cewu Lu, S. Tong

{"title":"Graspness Discovery in Clutters for Fast and Accurate Grasp Detection","authors":"Chenxi Wang, Haoshu Fang, Minghao Gou, Hongjie Fang, Jin Gao, Cewu Lu, S. Tong","doi":"10.1109/ICCV48922.2021.01566","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01566","url":null,"abstract":"Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of current grasp pose detection methods. In this paper, we propose \"graspness\", a quality based on geometry cues that distinguishes graspable area in cluttered scenes. A look-ahead searching method is proposed for measuring the graspness and statistical results justify the rationality of our method. To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process. Extensive experiments verify the stability, generality and effectiveness of our graspness model, allowing it to be used as a plug-and-play module for different methods. A large improvement in accuracy is witnessed for various previous methods after equipping our graspness model. Moreover, we develop GSNet, an end-to-end network that incorporate our graspness model for early filtering of low quality predictions. Experiments on a large scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30 + AP) and achieves a high inference speed.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"107 1","pages":"15944-15953"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75696066","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: 34

FFT-OT: A Fast Algorithm for Optimal Transportation FFT-OT:一种快速最优运输算法

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00622

Na Lei, X. Gu

引用次数: 6

Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories 基于卫星图像和GPS轨迹推断高分辨率交通事故风险地图

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01176

Songtao He, M. Sadeghi, S. Chawla, Mohammad Alizadeh, Harinarayanan Balakrishnan, Sam Madden

引用次数: 11

Ultra-High-Definition Image HDR Reconstruction via Collaborative Bilateral Learning 基于协同双边学习的超高清图像HDR重建

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00441

Zhuo Zheng, Wenqi Ren, Xiaochun Cao, Tao Wang, Xiuyi Jia

{"title":"Ultra-High-Definition Image HDR Reconstruction via Collaborative Bilateral Learning","authors":"Zhuo Zheng, Wenqi Ren, Xiaochun Cao, Tao Wang, Xiuyi Jia","doi":"10.1109/ICCV48922.2021.00441","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.00441","url":null,"abstract":"Existing single image high dynamic range (HDR) reconstruction methods attempt to expand the range of illuminance. They are not effective in generating plausible textures and colors in the reconstructed results, especially for high-density pixels in ultra-high-definition (UHD) images. To address these problems, we propose a new HDR reconstruction network for UHD images by collaboratively learning color and texture details. First, we propose a dual-path network to extract the content and chromatic features at a reduced resolution of the low dynamic range (LDR) input. These two types of features are used to fit bilateral-space affine models for real-time HDR reconstruction. To extract the main data structure of the LDR input, we propose to use 3D Tucker decomposition and reconstruction to prevent pseudo edges and noise amplification in the learned bilateral grid. As a result, the high-quality content and chromatic features can be reconstructed capitalized on guided bilateral upsampling. Finally, we fuse these two full-resolution feature maps into the HDR reconstructed results. Our proposed method can achieve real-time processing for UHD images (about 160 fps). Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art HDR reconstruction approaches on public benchmarks and real-world UHD images.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"48 1","pages":"4429-4438"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80025591","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: 18

Learning High-Fidelity Face Texture Completion without Complete Face Texture 学习高保真的面部纹理完成没有完整的面部纹理

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01373

Jongyoo Kim, Jiaolong Yang, Xin Tong

引用次数: 11

Neural Image Compression via Attentional Multi-scale Back Projection and Frequency Decomposition 基于注意多尺度反投影和频率分解的神经图像压缩

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01441

Ge Gao, P. You, Rong Pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, Ho-Jun Lee

{"title":"Neural Image Compression via Attentional Multi-scale Back Projection and Frequency Decomposition","authors":"Ge Gao, P. You, Rong Pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, Ho-Jun Lee","doi":"10.1109/ICCV48922.2021.01441","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01441","url":null,"abstract":"In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts. Despite the great progress, current methods still have limitations in preserving fine spatial details for optimal reconstruction, especially at low compression rates. We make three contributions in tackling this issue. First, we develop a novel back projection method with attentional and multi-scale feature fusion for augmented representation power. Our back projection method recalibrates the current estimation by establishing feedback connections between high-level and low-level attributes in an attentional and discriminative manner. Second, we propose to decompose the input image and separately process the distinct frequency components, whose derived latents are recombined using a novel dual attention module, so that details inside regions of interest could be explicitly manipulated. Third, we propose a novel training scheme for reducing the latent rounding residual. Experimental results show that, when measured in PSNR, our model reduces BD-rate by 9.88% and 10.32% over the state-of-the-art method, and 4.12% and 4.32% over the latest coding standard Versatile Video Coding (VVC) on the Kodak and CLIC2020 Professional Validation dataset, respectively. Our approach also produces more visually pleasant images when optimized for MS-SSIM. The significant improvement upon existing methods shows the effectiveness of our method in preserving and remedying spatial information for enhanced compression quality.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"28 1","pages":"14657-14666"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80293685","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: 45

A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction 随机视频预测的层次变分神经不确定性模型

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00961

Moitreya Chatterjee, N. Ahuja, A. Cherian

{"title":"A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction","authors":"Moitreya Chatterjee, N. Ahuja, A. Cherian","doi":"10.1109/ICCV48922.2021.00961","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.00961","url":null,"abstract":"Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high. Towards this end, we introduce Neural Uncertainty Quantifier (NUQ) - a stochastic quantification of the model’s predictive uncertainty, and use it to weigh the MSE loss. We propose a hierarchical, variational framework to derive NUQ in a principled manner using a deep, Bayesian graphical model. Our experiments on three benchmark stochastic video prediction datasets show that our proposed framework trains more effectively compared to the state-of-the-art models (especially when the training sets are small), while demonstrating better video generation quality and diversity against several evaluation metrics.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"125 1","pages":"9731-9741"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79022397","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: 9