Multi-level fusionFeature pyramidReceptive fieldObject detection datasetScale variation is one of the challenges in object detection. In this paper, we design a Multi-Level Feature Fusion Pyramid Network (MLFFPN) that can fuse features with different receptive fields so as to produce reliable object...
文献阅读:Lightweight multi-level feature difference fusion network for RGB-D-T salient object detection Brickman 我叫继林 来自专栏 · 文献阅读 1 人赞同了该文章 目录 收起 1研究动机 2创新点 3模型分析 4实验分析 5可改进的地方 时间:2023 期刊名称:Journal of King Saud University - Computer and...
目前单视角COD方法对背景干扰很敏感,难以检测到模糊的边界和可变形状的伪装对象。为了克服这些障碍,论文提出了一个基于行为的框架,称为多视角特征融合网络(Multi-view Feature Fusion Network, MFFN)。该框架模拟了人类在图像中寻找模糊物体的行为,即从多个角度、距离、视角进行观察。它背后的关键思想是通过数据增强生成...
including two sub-networks: a Temporal Alignment Network (TAN) fTAN and a Modulative Feature Fusion Network (MFFN) fMFFN . fTAN 接受参考框架 ILRt 和一个支撑框架 ILRt+i 作为输入,并将对应支撑框架的对齐特征 F~t+i 估计为,,然后,支撑框架的所有对齐特征连接为 受[33] 中的空间特征变换 (SFT—...
In addition, we propose a pixel-shuffle image fusion network (PSIFN) to aggregate multi-level contextual information and implement feature fusion to complete change map reconstruction. The conducted experimental results confirm that the MapsNet demonstrates better effectiveness and robustness in complex ...
Second, a multi-level feature fusion network which combines features in the same spatial sizes (intra-level) and from different spatial sizes (inter-level) is proposed to detect keypoint within the candidate area. The network can learn the spatial and semantic information and model the ...
To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant ...
To address this issue, we propose a novel architecture, called Feature Scaling Feature Fusion Network (FSFFNet) which alleviates the gap by successively fusing features at consecutive levels in multiple directions. For better dense pixel-level representation, we also employ a feature scaling technique...
论文网址:Multi-level feature disentanglement network for cross-dataset face forgery detection - ScienceDirect 发表期刊:Image and Vision Computing 影响因子:4.7(SCI 3区) Abstract ①过往不足:受到特定域训练数据的严重限制,由于域差距,在转移到跨数据集场景时通常表现不满意。 ②本文方法:提出了一种多层次特征...
所以提出M2Det模型,主要是Multi-Level Feature Pyramid Network(MLFPN)模块,其由Thinned U-shape Modules(TUM),Feature Fusion Modules(FFM)和Scale-wise Feature Aggregation Module (SFAM)组成,可以看出本文的工作量肯定不小。 2 相关模型 如下图所示,文中列举了四种风格的特征金字塔:SSD型、FPN型、STDN型,以及...