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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30983 条记录，以下是4751-4760 订阅

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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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StyleMix: Separating Content and Style for Enhanced Data Augmentation

StyleMix: Separating Content and Style for Enhanced Data Aug...

引用

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

作者： Hong, Minui Choi, Jinwoo Kim, Gunhee Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but mix or cut-andpaste the images. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair's class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-10, CIFAR-100 and ImageNet datasets show that StyleMix achieves better or comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks.

关键词： Training Deep learning computer vision Head Image color analysis Skin Robustness

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ScanFormer: Referring Expression Comprehension by Iteratively Scanning

ScanFormer: Referring Expression Comprehension by Iterativel...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Wei Su Peihan Miao Huanzhang Dou Xi Li College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Zhejiang-Singapore Innovation and Al Joint Research Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely Ref COCO, Ref COCO+, Ref COCO g, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

关键词： Visualization computer vision Accuracy Computational modeling Linguistics pattern recognition Iterative methods

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Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

Reciprocal Landmark Detection and Tracking with Extremely Fe...

引用

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

作者： Lin, Jianzhe Sahebzamani, Ghazal Luong, Christina Dezaki, Fatemeh Taheri Jafari, Mohammad Abolmaesumi, Purang Tsang, Teresa Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Univ British Columbia Dept Med Vancouver BC Canada Univ British Columbia Div Cardiol Vancouver BC Canada

ISBN: (纸本)9781665445092

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels;only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.

关键词： Training Location awareness computer vision Echocardiography Annotations Computational modeling Machine learning

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MIST: Multiple Instance Spatial Transformer

MIST: Multiple Instance Spatial Transformer

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

作者： Angles, Baptiste Jin, Yuhe Kornblith, Simon Tagliasacchi, Andrea Yi, Kwang Moo Univ Victoria Victoria BC Canada Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant K patches, and feeds these patches to a task-specific network - e.g., auto-encoder or classifier - to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurring structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.

关键词： Training Location awareness Transformers Image decomposition pattern recognition Proposals Task analysis

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NeuralFusion: Online Depth Fusion in Latent Space

NeuralFusion: Online Depth Fusion in Latent Space

引用

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

作者： Weder, Silvan Schonberger, Johannes L. Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is an online process, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers. The source code will be made available at https://***/weders/NeuralFusion.

关键词： Shape Superresolution Neural networks Real-time systems Encoding pattern recognition Task analysis

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Learned Initializations for Optimizing Coordinate-Based Neural Representations

Learned Initializations for Optimizing Coordinate-Based Neur...

引用

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

作者： Tancik, Matthew Mildenhall, Ben Wang, Terrance Schmidt, Divi Srinivasan, Pratul P. Barron, Jonathan T. Ng, Ren Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations.

关键词： Solid modeling Three-dimensional displays Limiting Shape Trajectory pattern recognition Task analysis

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Gradient-based Algorithms for Machine Teaching

Gradient-based Algorithms for Machine Teaching

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

作者： Wang, Pei Nagrecha, Kabir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9781665445092

The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing platforms, who tend to perform at least as well as machine learning systems. It is shown that, if allowed unbounded effort, the optimal student always learns the optimal predictor for a classification task. Hence, the role of the optimal teacher is to select the teaching set that minimizes student effort. This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population. The optimal teacher, denoted MaxGrad, is then shown to maximize the gradient of the risk on the set of new examples selected per iteration. MaxGrad teaching algorithms are finally provided for both binary and multiclass tasks, and shown to have some similarities with boosting algorithms. Experimental evaluations demonstrate the effectiveness of MaxGrad, which outperforms previous algorithms on the classification task, for both machine learning and human students from MTurk, by a substantial margin.

关键词： Training Machine learning algorithms Education Sociology Classification algorithms pattern recognition Risk management

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Point Cloud Upsampling via Disentangled Refinement

Point Cloud Upsampling via Disentangled Refinement

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

作者： Li, Ruihui Li, Xianzhi Heng, Pheng-Ann Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes the coarse output by adjusting the location of each point. Specifically, we design a pair of local and global refinement units in the spatial refiner to evolve a coarse feature map. Also, in the spatial refiner, we regress a per-point offset vector to further adjust the coarse outputs in fine scale. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts.

关键词： Surface reconstruction Three-dimensional displays Pipelines Generators Surface roughness Rough surfaces pattern recognition

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ReMix: Towards Image-to-Image Translation with Limited Data

ReMix: Towards Image-to-Image Translation with Limited Data

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

作者： Cao, Jie Hou, Luanxuan Yang, Ming-Hsuan He, Ran Sun, Zhenan CASIA CRIPAC NLPR Beijing Peoples R China CASIA CEBSIT Beijing Peoples R China UCAS AIR Beijing Peoples R China Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The generator learns to translate the in-between samples rather than memorizing the training set, and thereby forces the discriminator to generalize. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results. The ReMix method can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ReMix method achieve significant improvements.

关键词： Training Measurement Image quality computer vision Computational modeling Training data Generative adversarial networks

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