Introduction 对于image-text embedding learning,作者提出了 cross-modal projection matching (CMPM) loss 和 cross-modal projection classification (CMPC) loss。前者最小化两个模态特征投影分布的KL散度;后者基于norm-softmax损失,对模态A在模态B上的投影特征进行分类,进一步增强模态之间的契合度。 The Proposed Algor...
与原始的softmax loss相比,norm-softmax loss将所有权向量标准化为相同长度,以减少权值在区分不同样本时的影响。 如上图所示,softmax损失的分类结果依靠于\[\left\| {{W_k}} \right\|\left\| x \right\|\cos \left( {{\theta _k}} \right),\left( {k = 1,2} \right)\]。对于norm-softmax,...
Cross-Modal Projection Matching (CMPM) CMPM方案通过最小化不同模态的归一化匹配分布和投影兼容分布之间的Kullback-Leibler (KL)散度来建模跨模态相关 pi,j=exp(eiv⊤ξ(ejt))∑k=1nexp(eiv⊤ξ(ekt)) 其中,ξ(ejt)=ejt||ejt||, pi,j 可以认为是表示图像特征与文本特征匹配的概率 真实的匹...
On this basis, cross-modal projection matching constrain (CMPM) is introduced which minimizes the Kullback-Leibler divergence between feature projection matching distributions and label projection matching distributions, and label information is used to align similarities between low-dimensional features of ...
在低维特征学习的模块中采用对抗训练的方式对2种模态进行特征学习, 同时引入跨模态投影匹配(cross-modal projection matching, CMPM)[12]最小化特征投影匹配分布和标签投影匹配分布之间的KL(Kullback-Leibler)散度, 这样既能充分利用2种模态的语义知识, 又能保持模态间特征表示的分布一致性. 与特征学习步骤一样, ...
In order to compare the features extracted from different modalities, the features need to be modal- invariant. Various methods are proposed to reduce the cross-domain discrepancy by using adversarial loss, sharing a projection network, using triplet loss with pairs/triplets of different...
cross-modalmultimodal-deep-learningmultimodal-datasetstransformer-modelsmultimodal-pre-trained-modelvision-language-pretrainingmultimodal-applicationsmultimodal-pretext UpdatedOct 19, 2023 Code for journal paper "Learning Dual Semantic Relations with Graph Attention for Image-Text Matching", TCSVT, 2020. ...
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...
In this work, we implement different cross-modal learning schemes such as Siamese Network, Correlational Network and Deep Cross-Modal Projection Learning model and study their performance. We also propose a modified Deep Cross-Modal Projection Learning model that uses a different image feature extractor...
Zhang, Y., Lu, H.: Deep cross-modal projection learning for image-text matching. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp. 707–723. Munich, Germany (2018) Google Scholar Li, S., Cao, M....