Abstract: In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We …
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Recently, Zhang et al. proposed a guided aggregation network (GANet). Inspired by the cost aggregation in, a semi-global aggregation layer, which is a differentiable approximation of the semi-global matching, was introduced to capture the cost dependencies of …
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This study proposes a novel network called a dual guided aggregation network (Dual-GANet), which utilizes both left-to-right and right-to-left image matchings in network design and training …
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Thus, a Region-guided Spatial Feature Aggregation Network (RSFAN) for vehicle re-ID is proposed, which increases the robustness of the model by redistributing the expression of the salient and minor salient region-related information. Firstly, a Regional Localization (RL) module is proposed to automatically locate the salient and minor salient ...
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Bilateral Guided Aggregation Layer is a feature fusion layer for semantic segmentation that aims to enhance mutual connections and fuse different types of feature representation. It was used in the BiSeNet V2 architecture. Specifically, within the BiSeNet implementation, the layer was used to employ the contextual information of the Semantic Branch to guide the feature response of …
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Our final Consensus-guided Hierarchical Context Aggregation Network (CHCANet) was developed by incorporating the HCA framework with consensus guidance. Comprehensive experiments on large-scale indoor and outdoor datasets confirmed the effectiveness of our HCA framework and showed superior performance of CHCANet over state-of-the-art two-view ...
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An extension of the OWA operators which involves the use of triangular norms is introduced and a procedure for determining the measure of "orness" directly from the quantifier is suggested. We consider multicriteria aggregation problems where, rather than requiring all the criteria be satisfied, we need only satisfy some portion of the criteria. The proportion of the …
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We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks. Two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively are proposed, which can be used to replace the widely used 3D convolutional ...
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Furthermore, we propose a guided deformable aggregation based stereo matching network (GDANet) for balancing the efficiency and accuracy. It builds a fast 3D network to obtain the …
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Aiming at this problem, a difference-guided aggregation network (DGANet) is proposed, where two key modules are injected, i.e., a difference-guided aggregation module (DGAM) and a weighted metric module (WMM). The bitemporal features in DGAM are aggregated with the guidance of their differences, which focuses on their change relevance and ...
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Specifically, it presents a customized module, termed as Category Guided Aggregation (CGA), where it first identifies whether the neighbors belong to the same category with the center point or not, and then handles the two types of neighbors with two carefully-designed modules. Our CGA presents a general network module and could be leveraged in ...
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SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation Ted Lentsch* Zimin Xia* Holger Caesar Julian F. P. Kooij Intelligent Vehicles Group, Delft University of Technology, The Netherlands {T.deVriesLentsch,Z.Xia,H.Caesar,J.F.P.Kooij}@tudelft.nl Abstract This work addresses cross-view camera pose estimation,
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In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at…
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@inproceedings{zhang2019domaininvariant, title={Domain-invariant Stereo Matching Networks}, author={Feihu Zhang and Xiaojuan Qi and Ruigang Yang and Victor Prisacariu and Benjamin Wah and Philip Torr}, booktitle={Europe Conference on Computer Vision (ECCV)}, year={2020} } @inproceedings{Zhang2019GANet, title={GA-Net: Guided Aggregation Net for End-to-end …
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SliceMatch identifies for a ground-level image its camera's 3-DoF pose within a corresponding aerial image. It divides the camera's Field-of-View (FoV) into slices, i.e., vertical regions in (a).
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In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies …
WhatsApp: +86 18221755073
In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.
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In order to automate this complicated aggregation task, this paper employs the concept of K-Nearest-Neighbour guided Dependent Ordered Weighted Averaging (KNNDOWA) [4, 15], in which an argument (such as a measurement in this case) whose value is similar to its neighbours is deemed reliable and can be highly weighted. In contrast, an argument ...
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•We propose a novel item-guided aggregation framework for FedRec and the existing FedRec models can be regarded as the instantiation of our framework. •We propose a novel item semantic alignment mechanism for the federated cold-start recommendation, and the overall algorithm can be formulated into a unified federated opti-mization framework.
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Based on this datatset, we design a coding priors-guided aggregation network for VQE. 2.2 Video Enhancement Datasets In the past decade, numerous datasets [ 22, 35, 18, 37, 41, 10 ] have been developed for compressed video quality enhancement and they consist of HQ sequences and corresponding compression configurations, such as Low Delay P ...
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SliceMatch: Geometry-Guided Aggregation for Cross-View Pose Estimation. T de Vries Lentsch, Z Xia, H Caesar, JFP Kooij. CVPR, 17225-17234, 2023. 3: 2023: Ground-to-Aerial Image Matching for Vehicle Localization. Z Xia. TU Delft, 2024. 2024: Assignment 1-camera calibration.
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In this paper, we present a deep self-guided cost aggregation method used to obtain an accurate disparity map from a pair of stereo images.Conventional cost aggregation methods typically perform joint image filtering on each cost volume slice. Thus, a guidance image is necessary for the conventional methods to work effectively.
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GA-Net: Guided Aggregation Net for End-to-end Stereo Matching Brief Introduction We are formulating traditional geometric and optimization of stereo into deep neural networks ...
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Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy …
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Object tracking based on RGB images may fail when the color of the tracked object is similar to that of the background. Hyperspectral images with rich spectral features can provide more information for RGB-based trackers. However, there is no fusion tracking algorithm based on hyperspectral and RGB images. In this paper, we propose a reliability-guided aggregation …
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Stereo image dense matching, which plays a key role in 3D reconstruction, remains a challenging task in photogrammetry and computer vision. In addition to block-based matching, recent studies based on artificial neural networks have achieved great progress in stereo matching by using deep convolutional networks. This study proposes a novel network …
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To overcome the above two challenges, we propose a face inpainting model based on semantic feature-guided subspace pyramid aggregation. The model uses semantic parsing features of the missing face image to guide the inpainting, maintaining the consistency of semantic information and generating reasonable texture details at the same time.
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We propose an attention-guided aggregation stereo matching network, which can encode and integrate feature information multiple times in the entire network. The residual network based on the 2D channel attention block makes the extracted image features more robust and distinctive. The combination of the 3D channel attention block and the ...
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We propose a guided-attention and gated-aggregation network (GA2Net) for medical image segmentation. Our GA2Net comprises a pre-trained encoder, a bottleneck, and a decoder. In the bottleneck, we introduce a hierarchical gated feature aggregation module with the objective of generating spatial gates for multi-scale feature enhancement using ...
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Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture ...
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Figure 2 shows the framework of FedQL. It involves a Q-learning framework to generate weights used for aggregation. In a standard Q-learning system, after the agent receives current state (S_{t}) from the environment, it selects an action (A_{t}) according to Q values from the Q-table. Then the environment returns a reward or punishment to the agent, …
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We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks. In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose ...
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FedQL: Q-Learning Guided Aggregation for Federated Learning. Authors: Mei Cao, Mengying Zhao, Tingting Zhang, Nanxiang Yu, Jianbo Lu Authors Info & Claims. Algorithms and Architectures for Parallel Processing: 23rd International Conference, ICA3PP 2023, Tianjin, China, October 20–22, 2023, Proceedings, Part I.
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SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation [CVPR'23] [Paper] [arXiv] [Video] [BibTeX] Paper Abstract. This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which ...
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