aggregation may even deteriorate the performance, as elab-orated in Table 1 (b) later. This suggests that it is critical to model the motion during learning. In this work, we propose flow-guided feature aggrega-tion (FGFA). As illustrated in Figure 1, the feature extrac-tion network is applied on individual frames to produce the per-frame ...
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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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：(1)motion-guided spatial warping.feature mapwarp。(2)feature aggregation module.,,。 3.2 Model Design. Flow-guided warping.
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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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Quantifier guided aggregation using OWA operators. Ronald R. Yager, Corresponding Author. Machine Intelligence Institute; Iona 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. ...
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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 associated with a particular argument but with the ordered position of the arguments.
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Aggregation-Induced Emission (AIE) Nanoparticles-Assisted NIR-II Fluorescence Imaging-Guided Diagnosis and Surgery for Inflammatory Bowel Disease (IBD) Xiaoxiao Fan, Department of General Surgery, Sir Run Run Shaw Hospital, School of …
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Based on which, a Boundary guided Context Aggregation module (BCA) improved from Non-local network is further proposed to capture long-range dependencies …
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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
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Attention-guided chained context aggregation for semantic segmentation Image and Vision Computing ( IF 2.818) Pub Date :, DOI: 10.1016/j.imavis.2021.104309 Quan Tang, Fagui Liu, Tong Zhang, Jun Jiang, Yu Zhang
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The most basic type of statement provided by the Endeca Analytics API is the aggregation operation with GROUP BY, which buckets a set of Endeca records into a resulting set of aggregated Oracle Commerce Guide Search records. In most Analytics applications, all Analytics statements are aggregation operations.
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：Progressive Attention Guided Recurrent Network for Salient Object Detection 08:51:25 : 0 ：。
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Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting Abstract: Crowd counting aims to predict the number of people and generate the density map in the image. There are many challenges, including varying head scales, the diversity of crowd distribution across images and cluttered backgrounds.
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Flow Guided Featured Aggregation.,,,,t-10t+10t,,。
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Quantifier guided aggregation using OWA operators Quantifier guided aggregation using OWA operators Yager, Ronald R. 00:00:00 I. INTRODUCTION Starting with the classic work of Bellman and Zadeh,’ fuzzy set theory has been used as a tool to develop and model multicriteria decision problems. In this framework the criteria are represented as fuzzy subsets over …
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: Guided Aggregation Net for End-to-end Stereo Matching, ...
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Towards this end, we propose a novel local feature discovery, discriminative alignment and aggregation framework, inspired by the recent success of deep recurrent attention model. First, we develop a novel attribute-guided attentive network to sequentially discover informative parts/regions, by seeking a good registration between attentive ...
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Based on which, a Boundary guided Context Aggregation module (BCA) improved from Non-local network is further proposed to capture long-range dependencies between the pixels in the boundary regions and the ones inside the objects. By aggregating the context information along the boundaries, the inner pixels of the same category achieve mutual ...
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Flow-Guided Feature Aggregation for Video Object Detection This repository is implemented by Yuqing Zhu, Shuhao Fu, and Xizhou Zhu, when they are interns at MSRA. Introduction 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.
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Boundary Guided Context Aggregation for Semantic Segmentation. The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details. However, simply combing boundaries and the mainstream features cannot ensure a holistic ...
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Eduardo Fuentes, Ricardo Castro, Luis Astudillo, Gilda Carrasco, Marcelo Alarcón, Margarita Gutiérrez, Iván Palomo, " Bioassay-Guided Isolation and HPLC Determination of Bioactive Compound That Relate to the Antiplatelet Activity (Adhesion, Secretion, and Aggregation) from Solanum lycopersicum ", Evidence-Based Complementary and Alternative Medicine,. vol. 2012, Article ID 147031, 10 pages ...
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Flow-Guided Feature Aggregation for Video Object Detection This repository is implemented by Yuqing Zhu, Shuhao Fu, and Xizhou Zhu, when they are interns at MSRA.. Introduction 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.
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This work proposes the guided point contrastive loss to enhance the feature representation and model generalization ability in semi-supervised setting and designs the confidence guidance to ensure high-quality feature learning. Rapid progress in 3D semantic segmentation is inseparable from the advances of deep network models, which highly rely on large-scale annotated data for training.
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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 recog-
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To address this problem, we propose a novel paradigm called the Chained Context Aggregation Module (CAM). CAM gains features of various spatial scales through chain-connected ladder-style information flows. The features are then guided by… Expand
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Boundary-Guided-Context-Aggregation. Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"
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Attention-guided Chained Context Aggregation for Semantic Segmentation. 02/27/2020 ∙ by Quan Tang, et al. ∙ South China University of Technology International Student Union ∙ 0 ∙ share . Recent breakthroughs in semantic segmentation methods based on Fully Convolutional Networks (FCNs) have aroused great research interest.
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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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