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Consensus Maximisation Using Influences of Monotone Boolean Functions

Consensus Maximisation Using Influences of Monotone Boolean ...

引用

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

作者： Tennakoon, Ruwan Suter, David Zhang, Erchuan Chin, Tat-Jun Bab-Hadiashar, Alireza RMIT Univ Melbourne Vic Australia Edith Cowen Univ Perth WA Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.

关键词： computer vision Visualization Boolean functions Runtime Computational modeling Fitting Memory management

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Point2Skeleton: Learning Skeletal Representations from Point Clouds

Point2Skeleton: Learning Skeletal Representations from Point...

引用

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

作者： Lin, Cheng Li, Changjian Liu, Yuan Chen, Nenglun Choi, Yi-King Wang, Wenping Univ Hong Kong Hong Kong Peoples R China UCL London England Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781665445092

We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to use the insights of the medial axis transform (MAT) to capture the intrinsic geometric and topological natures of the original input points. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures. Extensive evaluations and comparisons show our method has superior performance and robustness. The learned skeletal representation will benefit several unsupervised tasks for point clouds, such as surface reconstruction and segmentation.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape Semantics Transforms Skeleton

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Shape and Material Capture at Home

Shape and Material Capture at Home

引用

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

作者： Lichy, Daniel Wu, Jiaye Sengupta, Soumyadip Jacobs, David W. Univ Maryland College Pk MD 20742 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few *** main technical contribution is the introduction of a recursive neural architecture, which can predict geometry and reflectance at 2(k) x 2(k) resolution given an input image at 2(k) x 2(k) and estimated geometry and reflectance from the previous step at 2(k-1) x 2(k -1). This recursive architecture, termed RecNet, is trained with 256 x256 resolution but can easily operate on 1024 x 1024 images during inference. We show that our method produces more accurate surface normal and albedo, especially in regions of specular highlights and cast shadows, compared to previous approaches, given three or fewer input images.

关键词： Reflectivity Geometry Training computer vision Image resolution Shape Neural networks

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Deep Gradient Projection Networks for Pan-sharpening

Deep Gradient Projection Networks for Pan-sharpening

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

作者： Xu, Shuang Zhang, Jiangshe Zhao, Zixiang Sun, Kai Liu, Junmin Zhang, Chunxia Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

ISBN: (纸本)9781665445092

Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.

关键词： Image resolution Satellites Stacking Neural networks Tools Iterative algorithms pattern recognition

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Harmonious Semantic Line Detection via Maximal Weight Clique Selection

Harmonious Semantic Line Detection via Maximal Weight Clique...

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

作者： Jin, Dongkwon Park, Wonhui Jeong, Seong-Gyun Kim, Chang-Su Korea Univ Seoul South Korea 42Dot Ai Seoul South Korea

ISBN: (纸本)9781665445092

A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://***/dongkwonjin/Semantic-Line-MWCS.

关键词： Measurement computer vision Codes Image edge detection Semantics Detectors Filtering algorithms

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Multimodal Motion Prediction with Stacked Transformers

Multimodal Motion Prediction with Stacked Transformers

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

作者： Liu, Yicheng Zhang, Jinghuai Fang, Liangji Jiang, Qinhong Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://***/mmTransformer.

关键词： Training computer vision Computational modeling Predictive models Network architecture Transformers Trajectory

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Regularizing Generative Adversarial Networks under Limited Data

Regularizing Generative Adversarial Networks under Limited D...

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

作者： Tseng, Hung-Yu Jiang, Lu Liu, Ce Yang, Ming-Hsuan Yang, Weilong Google Res Mountain View CA USA Univ Calif Merced Merced CA 95343 USA Waymo Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://***/google/lecam-gan.

关键词： Training computer vision Codes Computational modeling Training data Benchmark testing Fasteners

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Isometric Multi-Shape Matching

Isometric Multi-Shape Matching

引用

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

作者： Gao, Maolin Laehner, Zorah Thunberg, Johan Cremers, Daniel Bernard, Florian Tech Univ Munich Munich Germany Halmstad Univ Halmstad Sweden Univ Siegen Siegen Germany

ISBN: (纸本)9781665445092

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi-matchings that are by construction provably cycle-consistent. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching.

关键词： computer vision Three-dimensional displays Shape Linear programming Complexity theory Object tracking Optimization

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Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervi...

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

作者： Taherkhani, Fariborz Dabouei, Ali Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665445092

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.

关键词： Measurement Training Clustering algorithms Semisupervised learning Data models pattern recognition Convolutional neural networks

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Shelf-Supervised Mesh Prediction in the Wild

Shelf-Supervised Mesh Prediction in the Wild

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

作者： Ye, Yufei Tulsiani, Shubham Gupta, Abhinav Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We aim to infer 3D shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems (i.e. `shelf-supervised'). We first infer a volumetric representation in a canonical frame, along with the camera pose. We enforce the representation geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from image collections and per-instance reconstruction in finer details. We examine the method on both synthetic and the real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.

关键词： Visualization Three-dimensional displays Shape Scalability Lighting Predictive models Rendering (computer graphics)

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