Cross-Modal Encoder 三个独立模态的输出信息直接进行concat(在序列维度上进行concat),作为Cross-Modal Encoder的输入。 Cross-Modal Decoders Text/Vision decoder负责文本/图像的重建,并通过其完成对应的下游任务。Text Decoder采用了类似Transformer decoder的结构。Vision Decoder采用了一个二级网络结构:在训练阶段,第一...
还是以 word2vec 为例,instance discrimination 简单的说就是要找到一个编码器 encoder(通常用于连续信号或不可枚举信号) 或者说一个词典 dictionary(比如离散输入的一个词语,类似一组基的集合可以是完备或者过完备的)来对我们要研究的信号进行编码后输出 embedding 向量。 这类问题目前聚焦在用 contrastive learning ...
In this work, a Multi-scale Gradient balanced Central Difference Convolution (MG-CDC) and a Graph convolutional network-based Language and Image Fusion (GLIF) for cross-modal encoder, called Graph-RefSeg, are designed. Specifically, in the shallow layer of the encoder, the MG-CDC captures ...
Cross-modal retrieval has become a topic of popularity, since multi-data is heterogeneous and the similarities between different forms of information are worthy of attention. Traditional single-modal methods reconstruct the original information and lack of considering the semantic similarity between differen...
This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used...
Cross-modal Retrieval with Correspondence Autoencoders The problem of cross-modal retrieval, e.g., using a text query to search for images and vice-versa, is considered in this paper. A novel model involving co... F Feng,X Wang,R Li - Acm Multimedia 被引量: 167发表: 2014年 Cross-...
1. 解释什么是cmx: CMX(Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers)是一种利用Transformer模型实现跨模态融合的方法,旨在提高RGB-X(其中X代表其他模态数据,如深度图、红外图像等)语义分割任务的性能。CMX通过融合来自不同模态的信息,使模型能够更全面地理解场景,从而提升分割的准确性和鲁棒...
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training Paper:https://ojs.aaai.org/index.php/AAAI/article/download/6795/6649 Code:https://github.com/microsoft/Unicoder 如图1 所示, 作者从给定的图像中,先用 faster RCNN 抽取 proposal,得到对应的特征和 label。针对这...
modal interaction module (MIM) with a spatial-wise cross-attention algorithm adaptively captures cross-modal feature information. Meanwhile, the channel interaction modules (CIM) further enhance the aggregation of different modal streams. In addition, we efficiently aggregated global multiscale information ...
CUDA_VISIBLE_DEVICES=<GPUs>python main_autoencoder.py \ --config"cfgs/autoencoder/act_dvae_with_pretrained_transformer.yaml"\ --exp_name or sh train_autoencoder.sh<GPU> Stage II, pretrain 3D Transformer student on ShapeNet by running: CUDA_VISIBLE_DEVICES...