GA-Net: Guided Aggregation Net for End-to-end Stereo Matching Topics. stereo stereo-matching sgm Resources. Readme License. MIT license Activity. Stars. 541 stars Watchers. 26 watching Forks. 135 forks Report repository Releases No releases published. Packages 0. No packages published . Languages. Cuda 45.2%; Python 44.0%;
TLDR. This study proposes incorporating the traditional semi-global aggregation method, which optimizes an energy function, as a layer in CNN models by backpropagating the gradient from the disparity map to the cost volume, and introduces an end-to-end stereo network that combines both local and semi-global aggregation. Expand.
Furthermore, a Multi-sources Feature Aggregation (MFA) module is proposed to enhance the quality of support frames, hence the feature representation of current frame could be improved. Finally, a Temporal Relation-Guided (TRG) module is proposed to improve the feature aggregation perception by supervising the semantic similarity relationships ...
As shown in Table 7, our method achieves 72 .6% mean IoU with 156 FPS. BiSeNetV2 achieves better segmentation results and higher inference speed than most other real-time methods. Compared to DFANet, BiSeNetV2 achieves higher segmentation results with comparable inference speed.
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 × Dmax × F, H: height, W: width, Dmax: max disparity, F: feature size) by concatenating features be-tween the stereo views, computed at different disparity val-ues.
Quantifier guided aggregation using OWA operators. R. Yager. Published in International Journal of… 1 January 1996. Mathematics, Computer Science. TLDR. 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 …
This work proposes an accurate and efficient network called Attention‐guided Aggregation and Error‐aware Enhancement Network (AAEE‐Net), which achieves state‐of‐the‐art performance with low inference time and qualitative results show that AAEE‐ net significantly improves predictions, especially for thin structures. Stereo matching is a …
QUANTIFIER GUIDED AGGREGATION 53 An essential feature of this aggregation is the reordering operation, a nonlin- ear operator, that is used in the process. Thus in the OWA aggregation the weights are not …
Star 541. master. README. MIT license. GANet. GA-Net: Guided Aggregation Net for End-to-end Stereo Matching. Brief Introduction. We are formulating traditional geometric …
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:
The low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sace the low-level details, which lea…
This work proposes an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2), and …
Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training …
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
Therefore, how to effectively utilize the existing modality images to synthesize missing modality image has become a hot research topic. 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 …
cient guided aggregation (GA) strategies, which include a semi-globalaggregation (SGA)layerand alocalguidedag-gregation(LGA)layer. BothGAlayerscanbeimplemented …
This architecture involves the following: (i) A detail branch, with wide channels and shallow layers to capture low-level details and generate high-resolution feature …
Therefore, in this paper, we propose a Relation-guided Multi-stage Feature Aggregation (RMFA) network for video object detection. First, a Multi-Stage Feature Aggregation (MSFA) framework is devised to aggregate the feature representation of global and local support frames in each stage. In this way, both global semantic …
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 to reduce the …
BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic …
Abstract: Nowadays, CNN-based stereo matching methods achieved remarkable performance, and how to efficiently exploit contextual information in cost aggregation stage is the key to improve performance. In this paper, we propose a simple yet efficient network named Hierarchical Context Guided Aggregation Network (HCGANet). Specifically, a …
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 …
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 …
In this paper, the guided patch cost aggregation module and the combination of guided disparity map upsampling and coarse-to-fine method for disparity refinement are introduced, and applying them to real-time stereo matching, …
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 ...
In this paper, we proposed a Self-Guided Multiple Information Aggregation Network (SG-MIAN). Our backbone network MIAN utilizes the Multiple Spatial Perceptron (MSP) solely using classification information as guidance to discriminate the key classification features of lesion areas, and thereby performing more accurate localization …
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 bottom-up manner ...
Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of fea-ture 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 pro-
To address this issue, this paper proposes to utilize different aggregation strategies between the same category and different categories. 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 ...
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.
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).
Then, an edge-guided interaction module (EGI) is further designed to achieve feature enhancement by embedding edge information into the saliency branch as the spatial weights. In addition, two specific aggregation modules are proposed for the progressive fusion of multi-level features in the above two streams, thus making full use …
Guided by an attention mechanism, and with the help of an effective multi-level feature aggregation strategy, the proposed model can fully utilize and excavate various visual clues, such as edge context cues, high-level semantic information, etc., and finally identify the camouflage objects more accurately from a wide variety of challenging ...
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 …
SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation. 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 …
