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A Dual Iterative Refinement Method for Non-rigid Shape Matching

A Dual Iterative Refinement Method for Non-rigid Shape Match...

引用

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

作者： Xiang, Rui Lai, Rongjie Zhao, Hongkai UC Dept Math Irvine CA 92697 USA Rensselaer Polytech Inst Dept Math Troy NY 12181 USA Duke Univ Dept Math Durham NC 27706 USA

ISBN: (纸本)9781665445092

In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration. In each DIR iteration, starting from current correspondence, a zoom-in process at each point is used to select well matched anchor pairs by a local mapping distortion criterion. These selected anchor pairs are then used to align spectral features (or other appropriate global features) whose dimension adaptively matches the capacity of the selected anchor pairs. Thanks to the effective combination of complementary information in a data-adaptive way, DIR is not only efficient but also robust to render accurate results within a few iterations. By choosing appropriate dual features, DIR has the flexibility to handle patch and partial matching as well. Our comprehensive experiments on various data sets demonstrate the superiority of DIR over other state-of-the-art methods in terms of both accuracy and efficiency.

关键词： computer vision Image analysis Shape Stability criteria Feature extraction Distortion Iterative methods

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Shadow Removal via Global Residual Free Unet and Shadow Generation

Shadow Removal via Global Residual Free Unet and Shadow Gene...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dong Li Xin Lu Yurui Zhu Xi Wang Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing shadow removal methods face challenges when confronted with real-world scenes, particularly when dealing with complex background information and high-resolution images. To address these issues, we present a shadow removal framework based on shadow generation and Global Residual Free Unet (GRFUnet), which improves shadow removal from both data and network perspectives. For data enhancement, we train a generative adversarial network for producing shadow masks, which are then multiplied with clean images to obtain new shadow images. The additional shadow data produced by this structured generation approach allows for ample constraining of the network in color aspects, thereby enhancing color consistency in the images. In terms of network architecture, we design the Global Residual Free Unet that employs a convolutional framework to sequentially conduct Spatial Interaction and Channel Evolution for effective feature extraction. Moreover, we eliminate the commonly used Global residual connections in image restoration, as we discern their in-effectiveness for the non-additive task of shadow removal. Through this methodology, we achieve excellent shadow removal results, both qualitatively and quantitatively. Our method achieves the highest PSNR in the NTIRE24-Image Shadow Removal Challenge and achieves commendable and balanced outcomes across two tracks—placing third in the fidelity track and fourth in the perceptual track.

关键词： computer vision Image color analysis conferences computer architecture Network architecture Feature extraction Generative adversarial networks

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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Multiresolution Knowledge Distillation for Anomaly Detection

Multiresolution Knowledge Distillation for Anomaly Detection

引用

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

作者： Salehi, Mohammadreza Sadjadi, Niousha Baselizadeh, Soroosh Rohban, Mohammad H. Rabiee, Hamid R. Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781665445092

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the "distillation" of features at various layers of an expert network, which is pre-trained on ImageNet, into a simpler cloner network to tackle both issues. We detect and localize anomalies using the discrepancy between the expert and cloner networks' intermediate activation values given an input sample. We show that considering multiple intermediate hints in distillation leads to better exploitation of the expert's knowledge and a more distinctive discrepancy between the two networks, compared to utilizing only the last layer activation values. Notably, previous methods either fail in precise anomaly localization or need expensive region-based training. In contrast, with no need for any special or intensive training procedure, we incorporate interpretability algorithms in our novel framework to localize anomalous regions. Despite the striking difference between some test datasets and ImageNet, we achieve competitive or significantly superior results compared to SOTA on MNIST, F-MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two other medical datasets on both anomaly detection and localization.

关键词： Training Location awareness Knowledge engineering computer vision Image resolution pattern recognition Task analysis

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PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers

PCLs: Geometry-aware Neural Reconstruction of 3D Pose with P...

引用

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

作者： Yu, Frank Salzmann, Mathieu Fua, Pascal Rhodin, Helge Univ British Columbia Vancouver BC Canada Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665445092

Local processing is an essential feature of CNNs and other neural network architectures-it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positions in the image. We introduce Perspective Crop Layers (PCLs)-a form of perspective crop of the region of interest based on the camera geometry- and show that accounting for the perspective consistently improves the accuracy of state-of-theart 3D pose reconstruction methods. PCLs are modular neural network layers, which, when inserted into existing CNN and MLP architectures, deterministically remove the location-dependent perspective effects while leaving end-to-end training and the number of parameters of the underlying neural network unchanged. We demonstrate that PCL leads to improved 3D human pose reconstruction accuracy for CNN architectures that use cropping operations, such as spatial transformer networks (STN), and, somewhat surprisingly, MLPs used for 2D-to-3D keypoint lifting. Our conclusion is that it is important to utilize camera calibration information when available, for classical and deep-learning-based computer vision alike. PCL offers an easy way to improve the accuracy of existing 3D reconstruction networks by making them geometry-aware.

关键词： Training computer vision Three-dimensional displays Neural networks Crops computer architecture Cameras

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Generalizing to the Open World: Deep Visual Odometry with Online Adaptation

Generalizing to the Open World: Deep Visual Odometry with On...

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

作者： Li, Shunkai Wu, Xin Cao, Yingdian Zha, Hongbin Peking Univ Sch EECS Key Lab Machine Percept MOE PKU SenseTime Machine Vis Joint Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data makes them difficult to generalize to new scenes. In this paper, we propose an online adaptation framework for deep VO with the assistance of scene-agnostic geometric computations and Bayesian inference. In contrast to learning-based pose estimation, our method solves pose from optical flow and depth while the single-view depth estimation is continuously improved with new observations by online learned uncertainties. Meanwhile, an online learned photometric uncertainty is used for further depth and pose optimization by a differentiable Gauss-Newton layer. Our method enables fast adaptation of deep VO networks to unseen environments in a self-supervised manner. Extensive experiments including Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that our method achieves state-of-the-art generalization ability among self-supervised VO methods.

关键词： Training Optical filters Uncertainty Pose estimation Bayes methods pattern recognition Optimization

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HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

HistoGAN: Controlling Colors of GAN-Generated and Real Image...

引用

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

作者： Afifi, Mahmoud Brubaker, Marcus A. Brown, Michael S. York Univ N York ON Canada

ISBN: (纸本)9781665445092

While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images' colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modifi- cation of the recent StyleGAN architecture [31] to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image's content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images' colors while producing more compelling results compared to existing alternative strategies.

关键词： Graphics Histograms computer vision Image color analysis Image synthesis Semantics Generative adversarial networks

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Image-to-image Translation via Hierarchical Style Disentanglement

Image-to-image Translation via Hierarchical Style Disentangl...

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

作者： Li, Xinyang Zhang, Shengchuan Hu, Jie Cao, Liujuan Hong, Xiaopeng Mao, Xudong Huang, Feiyue Wu, Yongjian Ji, Rongrong Xiamen Univ Sch Informat Xiamen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Tencent Youtu Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://***/imlixinyang/HiSD.

关键词： Training computer vision Codes Scalability Process control Semisupervised learning pattern recognition

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Revisiting Superpixels for Active Learning in Semantic Segmentation with Realistic Annotation Costs

Revisiting Superpixels for Active Learning in Semantic Segme...

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

作者： Cai, Lile Xu, Xun Liew, Jun Hao Foo, Chuan Sheng Inst Infocomm Res Singapore Singapore Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs. A key design choice for region-based AL is whether to use regularly-shaped regions (e.g., rectangles) or irregularly-shaped region (e.g., superpixels). In this work, we address this question under realistic, click-based measurement of annotation costs. In particular, we revisit the use of super-pixels and demonstrate that the inappropriate choice of cost measure (e.g., the percentage of labeled pixels), may cause the effectiveness of the superpixel-based approach to be under-estimated. We benchmark the superpixel-based approach against the traditional "rectangle+polygon"-based approach with annotation cost measured in clicks, and show that the former outperforms on both Cityscapes and PASCAL VOC. We further propose a class-balanced acquisition function to boost the performance of the superpixel-based approach and demonstrate its effectiveness on the evaluation datasets. Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.

关键词： Deep learning computer vision Costs Annotations Atmospheric measurements Semantics Benchmark testing

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Causal Attention for vision-Language Tasks

Causal Attention for Vision-Language Tasks

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

作者： Yang, Xu Zhang, Hanwang Qi, Guojun Cai, Jianfei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Futurewei Technol Santa Clara CA USA Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9781665445092

We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization. As the confounder is unobserved in general, we use the front-door adjustment to realize the causal intervention, which does not require any knowledge on the confounder. Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention. CATT abides by the QK-V convention and hence can replace any attention module such as top-down attention and self-attention in Transformers. CATT improves various popular attention-based vision-language models by considerable margins. In particular, we show that CATT has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT [57], which uses fewer data and less computational power, comparable to the heavier UNITER [14]. Code is published in https://***/yangxuntu/lxmertcatt.

关键词： computer vision Correlation Codes Computational modeling Training data Transformers Data models

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