The algorithm leverages a dual encoder-decoder network to extract multi-scale information from the image. It then employs a dual decoder network to restore the feature map to the original image, achieving precise road extraction. Additionally, a new module, the Global Fusion Module (GFM), is ...
which effectively increases the size of the feature maps. Afterwards, the features maps of the encoder skip-connections are concatenated with the output feature maps. The skip-connections from the first encoder are used in the first decoder. Nevertheless, the skip...
we propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplica...
Next, the key regions and original frames are fed into two separate encoders of a dual-channel autoencoder, extracting features from both the key regions and the original video frames. The fused features are then inputted into a self-attention decoder for frame reconstruction. Finally, the ...
[2] proposed an inpainting method based on an encoder-decoder architecture and contextual semantic features, known as context encoders (CE). However, the restored areas often suffer from low resolution and lack consistency in surrounding areas. Besides, this method requires that the damaged areas ...
DeepUTF: Locating Transcription Factor Binding Sites and Predicting Motifs via Interpretable Dual-Channel Encoder-Decoder Structure - YuBinLab-QUST/DeepUTF
encoder-decoder 结构 问:这样做的缺点是什么? 答:以上方法可以捕获不同尺度的目标,但是它没有利用目标之间的关系,这些对于场景表达也是重要的。 使用递归神经网络来捕捉长期依赖关系: 例如2D的LSTM。 问:这样做的缺点是什么? 答:有效性在很大程度上依赖于长期记忆的学习结果。 三、创新点 3.1 概述 要点: 这篇...
以前的语义图像合成方法通常通过编码器-解码器(encoder-decoder)架构来生成图像,语义标签图作为其输入。然而,如 [21] 所示,由于使用了常见的归一化层,如实例归一化(instance normalization) [29],平面分割图(flat segmentation maps)将逐渐消失。为了解决这个问题,人们提出了空间自适应归一化 (spatially-adaptive normali...
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation;Kyunghyun Cho ,Dzmitry Bahdanau,Yoshua Bengio(研究组) 本文采用的任然是“解码-编码”的结构,分别采用一个RNN做为编码和解码器,第一个RNN把源语言句子映射成一个固定长度的输出隐向量,然后第二个RNN把固定的长度的隐向...
Encoder-Decoder架构、Multi-head注意力机制、Dropout和残差网络等都是Bayesian神经网络的具体实现;基于Transformer各种模型变种及实践也都是基于Bayesian思想指导下来应对数据的不确定性;混合使用各种类型的Embeddings来提供更好Prior信息其实是应用Bayesian思想来集成处理信息表达的不确定性、各种现代NLP比赛中高分的作品也大多是...