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guided aggregation
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IFedRec: Item-Guided Federated Aggregation for Cold …
•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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Deep self-guided cost aggregation for stereo matching
To the best of our knowledge, the proposed work is both the first self-guided and first local cost aggregation method based on the deep learning approach. In summary, this paper makes the following contributions. -We introduce a self-guided cost aggregation method for stereo matching that does not require any guidance color image.-
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[2211.14651] SliceMatch: Geometry-guided Aggregation for …
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 consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extractors …
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Attention guided multi-level feature aggregation network …
In this work, we propose an attention-guided multi-level feature aggregation network AGMFA for camouflaged object detection, which consists of several simple yet effective modules including FEM, ECM, and FAM. Guided by an attention mechanism, and with the help of an effective multi-level feature aggregation strategy, the proposed …
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GitHub
@inproceedings{Zhang2019GANet, title={GA-Net: Guided Aggregation Net for End-to-end Stereo Matching}, author={Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip HS}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={185--194}, year={2019} } About. GA-Net: Guided Aggregation …
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arXiv:2403.10362v1 [eess.IV] 15 Mar 2024
ing priors-guided aggregation network, named CPGA. The CPGA consists of three modules: the inter-frame temporal aggregation (ITA) module, the multi-scale non-local aggre-gation (MNA) module and the quality enhancement (QE) module. Specifically, the ITA module explores the inter-frame correlations among the multiple compressed frames
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CVPR 2019 Open Access Repository
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching. In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural …
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Quantifier Guided Aggregation Using OWA Operators
Quantifier Guided Aggregation Using OWA Operators Ronald R. Yager Machine Intelligence Institute; lona College, New Rochelle, New York 10801 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 critera required is ...
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Knowledge graph representation learning with relation-guided
The relation-guided aggregation module aims to capture rich attributes of entities, which generates the sub-structures according to relation types, and aggregates semantic information among them. To dig out the contribution of different relation types to the central entity, relation-guided interaction module is proposed to calculate the ...
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BiSeNet V2: Bilateral Network with Guided Aggregation for …
We design a guided aggregation layer to enhance mutual connections and fuse both types of feature representation. Moreover, 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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CVPR 2019 Open Access Repository
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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FedQL: Q-Learning Guided Aggregation for Federated …
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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GA-Net: Guided Aggregation Net for End-to-end Stereo …
GA-Net is a novel deep learning model for estimating disparities from stereo images. It uses two guided aggregation layers to capture local and global cost …
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Dual Guided Aggregation Network for Stereo Image Matching
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 …
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Flow-Guided Feature Aggregation for Video Object Detection
Flow-Guided Feature Aggregation (FGFA) is initially described in an ICCV 2017 paper.It provides an accurate and end-to-end learning framework for video object detection. The proposed FGFA method, together with our previous work of Deep Feature Flow, powered the winning entry of ImageNet VID 2017.It is worth noting that:
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Language-Guided Visual Aggregation Network for Video …
To address this issue, we introduce a Language-Guided Visual Aggregation (LGVA) network. It employs CLIP as an effective feature extractor to obtain language-aligned visual features with different granularities and avoids resource-intensive video pre-training. The LGVA network progressively aggregates visual information in a …
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BiSeNet V2: Bilateral Network with Guided Aggregation for
BiSeNet V2 is a bilateral network that separates spatial details and categorical semantics for high accuracy and efficiency. It uses a guided aggregation …
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RANet: A Reliability-Guided Aggregation Network for …
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 …
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Relation-Guided Multi-stage Feature Aggregation Network …
To solve the above-mentioned problems of two kinds of feature aggregation strategies, in this paper, we propose a relation-guided multi-stage feature aggregation network for video object detection task. The main contributions of this paper could be summarized as follows: (1) A multi-stage feature aggregation framework is devised.
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Flow-Guided Feature Aggregation for Video Object Detection
We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy.
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Dual-modal imaging-guided agent based on NIR-II aggregation …
Dual-modal imaging-guided agent based on NIR-II aggregation-induced emission luminogens with balanced phototheranostic performance Chem Sci. 2024 Jun 7;15 (28):10969 ... These nanoparticles were applied to fluorescence-photothermal dual-mode imaging-guided photothermal ablation in a HeLa tumor xenograft mouse model, …
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Multi-Modality MR Image Synthesis via Confidence-Guided Aggregation …
In this paper, we propose a novel confidence-guided aggregation and cross-modality refinement network (CACR-Net) for multi-modality MR image synthesis, which effectively utilizes complementary and correlative information of multiple modalities to synthesize high-quality target-modality images. Specifically, to effectively utilize the ...
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Attention-guided aggregation stereo matching network
We propose an attention-guided aggregation stereo matching network, which can encode and integrate feature information multiple times in the entire network. The …
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Guided aggregation and disparity refinement for real
Download Citation | Guided aggregation and disparity refinement for real-time stereo matching | Stereo matching methods based on convolution neural network (CNN) often face challenges such as edge ...
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Graph convolutional network with tree-guided anisotropic …
A tree-guided anisotropic aggregation strategy is proposed for message passing. Within this strategy, the message is passed along paths in a hierarchical tree-like hypergraph with substructures of the original graph as its nodes. In parallel, the intensity of message passing is constrained adaptively by an effective gating mechanism.
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Guided aggregation and disparity refinement for real …
convolutional networks, we propose a guided patch cost aggregation module (GPA) that generates a more precise initial disparity map for textureless areas. These modules complement each other and are efficient, resulting in an accurate and lightweight framework for stereo matching. Experimental results demonstrate that our algorithm has ...
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GA-Net: Guided Aggregation Net for End-to-end Stereo …
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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CGA-Net: Category Guided Aggregation for Point Cloud …
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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Depth-Guided Aggregation for Real-Time Binocular Depth …
We propose an efficient and flexible cost aggregation module that supplements residual information with high-resolution cost volumes. By replacing some computationally demanding 3-D convolutional layers with depth-guided excitation, we maintain accuracy while effectively controlling model computation. Alongside the …
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Attention-guided aggregation stereo matching network
Drawing lessons from traditional cost aggregation ideas, the guided matching cost aggregation strategy [28] and the intra-scale and inter-scale adaptive aggregation network [29] have been successively proposed to replace the 3D convolution in order to speed up the inference speed of the network model. 2.4. Attention mechanism
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Simulated Data-Guided Incremental SAR ATR Through Feature Aggregation …
Applying synthetic aperture radar automatic target recognition (SAR ATR) in open scenario based on deep learning (DL) is challenging due to the difficulty in incrementally recognizing new targets with limited samples. To address this challenge, we introduce simulated data that reflects the structure and scattering features of the new …
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Range Guided Depth Refinement and Uncertainty-Aware Aggregation …
In this paper, we present a framework of view synthesis, including range guided depth refinement and uncertainty-aware aggregation based novel view synthesis. We first propose a novel depth refinement method to improve the quality and robustness of the depth map reconstruction. To that end, we use a range prior to constrain the estimated …
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DBFGAN: Dual Branch Feature Guided Aggregation …
This paper proposes a Dual Branch Feature Guided Aggregation Network (DBFGAN) composed of CNN and Transformer to solve the above problems. Transformer is more adept at gathering worldwide information than convolution; On the one hand, convolution has better translation invariance than Transformer.
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MangroveSeg: Deep-Supervision-Guided Feature Aggregation …
We proposed a deep-supervision-guided feature aggregation network based on a U-shape structure with ResNet as the backbone network for mangrove detection and segmentation. The construction of the dataset was achieved through the utilization of QGIS software version 3.28 (QGIS is released under the GPL Version 2 or any later version). ...
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GA-Net: Guided Aggregation Net for End-to-end Stereo …
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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[2403.10362] CPGA: Coding Priors-Guided Aggregation …
To remedy this problem, we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically, the ITA module …
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