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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4861-4870 订阅

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Local Attention Pyramid for Scene Image Generation

Local Attention Pyramid for Scene Image Generation

引用

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

作者： Shim, Sang-Heon Hyun, Sangeek Bae, DaeHyun Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.

关键词： Measurement Deep learning Visualization Image segmentation computer vision Image analysis Image synthesis

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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartp...

引用

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

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Chen, Sheng Xia, Xin Liu, Zhaoyan Zhang, Yuwei Zhu, Feng Li, Jiashi Xiao, Xuefeng Tian, Yuan Wu, Xinglong Kyrkou, Christos Chen, Yixin Zhang, Zexin Peng, Yunbo Lin, Yue Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Kumar, Himanshu Ge, Chao Wu, Pei-Lin Du, Jin-Hua Batutin, Andrew Federico, Juan Pablo Lyda, Konrad Khojoyan, Levon Thanki, Abhishek Paul, Sayak Siddiqui, Shahid Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland AI Witchlabs Lausanne Switzerland ByteDance Inc Beijing Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Netease Games AI Lab Beijing Peoples R China Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India Indian Inst Technol Jodhpur Karwar India Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China DataArt Inc New York NY USA PyImageSearch Mumbai Maharashtra India Univ Cyprus KIOS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665448994

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Image analysis Computational modeling conferences Cameras Real-time systems

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Semantic Image Matting

Semantic Image Matting

引用

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

作者： Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-theart performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://***/nowsyn/ SIM.

关键词： Hair computer vision Codes Semantics Benchmark testing Data mining Optimization

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RoutedFusion: Learning Real-time Depth Map Fusion

RoutedFusion: Learning Real-time Depth Map Fusion

引用

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

作者： Weder, Silvan Schonberger, Johannes Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Zurich Switzerland Microsoft Albuquerque NM USA

ISBN: (纸本)9781728171685

The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capable machine learning-based method for depth map fusion. Similar to the seminal depth map fusion approach by Curless and Levoy, we only update a local group of voxels to ensure real-time capability. Instead of a simple linear fusion of depth information, we propose a neural network that predicts non-linear updates to better account for typical fusion errors. Our network is composed of a 2D depth routing network and a 3D depth fusion network which efficiently handle sensor-specific noise and outliers. This is especially useful for surface edges and thin objects for which the original approach suffers from thickening artifacts. Our method outperforms the traditional fusion approach and related learned approaches on both synthetic and real data. We demonstrate the performance of our method in reconstructing fine geometric details from noise and outlier contaminated data on various scenes.

关键词： computer vision

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Relative Pose from a Calibrated and an Uncalibrated Smartphone Image

Relative Pose from a Calibrated and an Uncalibrated Smartpho...

引用

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

作者： Ding, Yaqing Barath, Daniel Yang, Jian Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Swiss Fed Inst Technol Dept Comp Sci Comp Vis & Geometry Grp Zurich Switzerland Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic Lund Univ Ctr Math Sci Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly available real-world and new datasets. When combined with a recent robust estimator, they lead to results superior to the traditional solvers in terms of rotation, translation and focal length accuracy, while being notably faster.

关键词： Resistance Bundle adjustment computer vision Codes Tracking Cameras Sensors

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Exploring Compositional High Order pattern Potentials for Structured Output Learning

Exploring Compositional High Order Pattern Potentials for St...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Li, Yujia Tarlow, Daniel Zemel, Richard Univ Toronto Toronto ON M5S 3G4 Canada

ISBN: (纸本)9780769549897

When modeling structured outputs such as image segmentations, prediction can be improved by accurately modeling structure present in the labels. A key challenge is developing tractable models that are able to capture complex high level structure like shape. In this work, we study the learning of a general class of pattern-like high order potential, which we call Compositional High Order pattern Potentials (CHOPPs). We show that CHOPPs include the linear deviation pattern potentials of Rother et al. [] and also Restricted Boltzmann Machines (RBMs);we also establish the near equivalence of these two models. Experimentally, we show that performance is affected significantly by the degree of variability present in the datasets, and we define a quantitative variability measure to aid in studying this. We then improve CHOPPs performance in high variability datasets with two primary contributions: (a) developing a loss-sensitive joint learning procedure, so that internal pattern parameters can be learned in conjunction with other model potentials to minimize expected loss;and (b) learning an image-dependent mapping that encourages or inhibits patterns depending on image features. We also explore varying how multiple patterns are composed, and learning convolutional patterns. Quantitative results on challenging highly variable datasets show that the joint learning and image-dependent high order potentials can improve performance.

关键词： Boltzmann machines feature extraction image segmentation learning (artificial intelligence)

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Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning

Local Learning Matters: Rethinking Data Heterogeneity in Fed...

引用

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

作者： Mendieta, Matias Yang, Taojiannan Wang, Pu Lee, Minwoo Ding, Zhengming Chen, Chen Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ N Carolina Dept Comp Sci Charlotte NC USA Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-HD in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. To this end, we first present a systematic study informed by second-order indicators to better understand algorithm effectiveness in FL. Interestingly, we find that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Based on our findings, we further propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves competitive accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead.

关键词： Performance evaluation computer vision computer aided instruction Systematics Federated learning Distance learning Data models

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Maximum Consensus by Weighted Influences of Monotone Boolean Functions

Maximum Consensus by Weighted Influences of Monotone Boolean...

引用

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

作者： Zhang, Erchuan Suter, David Tennakoon, Ruwan Chin, Tat-Jun Bab-Hadiashar, Alireza Truong, Giang Gilani, Syed Zulqarnain EdithCowanUniv Ctr AI & ML Perth WA Australia Edith Cowan Univ Nutr & Hlth Innovat Res Inst Perth WA Australia RMIT Univ Melbourne Vic Australia Univ Adelaide Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure;and the distribution used for sampling to estimate the influence measure. This paper studies the concept of weighted influences for solving MaxCon. In particular, we study the Bernoulli measures. Theoretically, we prove the weighted influences, under this measure, of points belonging to larger structures are smaller than those of points belonging to smaller structures in general. We also consider another "natural" family of weighting strategies: sampling with uniform measure concentrated on a particular (Hamming) level of the cube. One can choose to have matching distributions: the same for defining the measure as for implementing the sampling. This has the advantage that the sampler is an unbiased estimator of the measure. Based on weighted sampling, we modify the algorithm of Tennakoon et al., and test on both synthetic and real datasets. We show some modest gains of Bernoulli sampling, and we illuminate some of the interactions between structure in data and weighted measures and weighted sampling.

关键词： Weight measurement computer vision Boolean functions Atmospheric measurements Machine vision Estimation Gain measurement

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clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

clDice - a Novel Topology-Preserving Loss Function for Tubul...

引用

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

作者： Shit, Suprosanna Paetzold, Johannes C. Sekuboyina, Anjany Ezhov, Ivan Unger, Alexander Zhylka, Andrey Pluim, Josien P. W. Bauer, Ulrich Menze, Bjoern H. Tech Univ Munich Munich Germany Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781665445092

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic;particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerline-Dice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.

关键词： Training computer vision Three-dimensional displays Network topology Roads Neurons Neural networks

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Neural Refinement for Absolute Pose Regression with Feature Synthesis

Neural Refinement for Absolute Pose Regression with Feature ...

引用

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

作者： Chen, Shuai Bhalgat, Yash Li, Xinghui Bin, Jia-Wang Li, Kejie Wang, Zirui Prisacariu, Victor Adrian Univ Oxford Act Vision Lab Oxford England Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350353006

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of- the-art single-image APR accuracy on indoor and outdoor datasets. Code will be released at https:// ***/ActivevisionLab/NeFeS.

关键词： Feature Distillation Neural Radiance Field Pose Regression Test-time Refinement Visual Re-Localization

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