Guided Aggregation

DBFGAN: Dual Branch Feature Guided Aggregation Network for …

(3) We designed the Feature Guided Aggregation Module(FGAM) to enable the two branches in the backbone network to guide and learn from each other. So that the model can accurately locate the change area, and maximize the recovery of …

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 …

SliceMatch: Geometry-guided Aggregation for Cross-View Pose …

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 extract dense features from the …

CPGA: Coding Priors-Guided Aggregation Network for …

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

GA-Net: Guided Aggregation Net for End-to …

GA-NetGC-NetSGM,,： Semi- Global Aggregation （） local guided aggregation ()。 SGA …

GA-Net: Guided Aggregation Net for End-to-end Stereo …

In this work, we propose two novel cost aggregation lay-ers for end-to-end stereo reconstruction to replace the use of 3D convolutions. Our solution considerably increases accu-racy, while …

OWA Operators in Decision Making

In 1988 Ronald R. Yager [14] introduced a new aggregation technique based on the ordered weighted averaging (OWA) operators. The goal of this paper is to present a short survey of of OWA operators ...

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 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 ...

FedQL: Q-Learning Guided Aggregation for Federated Learning

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, …

Knowledge graph representation learning with relation-guided

The relation-guided aggregation module separates the neighbors into multiple sub-structures, and then aggregates information from them according to different relation types. The relation-guided interaction module aims to dig out the contribution of different relation types, which calculates the importance of each relation to the central entity ...

BiSeNet V2: Bilateral Network with Guided Aggregation for Real …

Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy …

Fed-GFM-DG: A privacy-preserving framework for fundus image

The aggregation time for both FedAvg [18] and FedTMask is then measured, as shown in Table 10. Despite FedTMask taking slightly longer due to the increased algorithmic complexity, the aggregation time remains negligible compared to the time spent on local training and the overall time taken for a round.

BiSeNet V2

Guided aggregation and disparity refinement for real-time stereo

Therefore, the adaptive scale dilated convolution patch aggregation is used to expand the search range for aggregation. Guided patch cost aggregation is more likely to aggregate global and local features from multi-scale pyramid features, which can provide a more detailed initial disparity map for disparity refinement process, thereby improving ...

cvpr2023

SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation . -pv 3. OrienterNet: Visual Localization in 2D Public Maps with Neural Matching 3. MeshLoc: Mesh-Based Visual …

：BiSeNetV1v2

BiSeNetV2 Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation 。,,,。,。

GA-Net : Guided Aggregation Net for End-to-End Stereo …

：Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising —— CVPR 2023！,,（）。

【】BiSeNetV2

Guided Aggregation Layer： DetailSemanticGuided Aggregation Layer,SemanticDetail,2, …

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 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 …

FedQL: Q-Learning Guided Aggregation for Federated …

FedQL: Q-Learning Guided Aggregation for Federated Learning 265 can learn to set weight according to certain system state. The main contributions of this paper are as follows. – We propose to employ Q-learning to solve the aggregation problem in feder-ated learning, with the objective of fast convergence of the global model.

[2403.10362] CPGA: Coding Priors-Guided Aggregation Network …

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 aggregates temporal information ...

tudelft-iv/SliceMatch: Cross-View Camera Pose Estimation …

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 ...

(1)GA-Net（CVPR2019）

2019cvpr Oral. ：GA-Net: Guided Aggregation Net for End-to-end Stereo Matching ：,。,。

arXiv:1904.06587v1 [cs.CV] 13 Apr 2019

3. Guided Aggregation Net In this section, we describe our proposed guided aggre-gation network (GA-Net), including the guided aggregation (GA) layers and the improved network architecture. 3.1. Guided Aggregation Layers State-of-the-art end-to-end stereo matching neural nets such as [3,13] build a 4D matching cost volume (with size of H W D

Quantifier Guided Aggregation Using OWA Operators

QUANTIFIER GUIDED AGGREGATION 63 In the calculation of the weights we make considerable use of summations of the form uk we shall find it convenient to note these as Sj, thus j sj= k=2I u k * Using this notation we get wj = Q ($)- (9) …

FedSim: Similarity guided model aggregation for Federated Learning

The similarity-guided FedSim algorithm, introduced in this paper, decomposes FL aggregation into local and global steps. Clients with similar gradients are clustered to provide local aggregations, which thereafter can be globally aggregated to ensure better coverage whilst reducing variance.

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,《Knowledge graph representation learning with relation-guided aggregation and interaction》《Information Processing and Management》。 KGRL（RGAI）,（RGA）（RGI）。

BiSeNet V2: Bilateral Network with Guided Aggregation for Real …

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 ...

Guided-attention and gated-aggregation network for medical …

We propose GA2Net to capture complex shapes of the tissues for better segmentation. Our HGFA enhances the most relevant feature information for pixel-precise …