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...
3.2 Cross-Modal Projection Matching We introduce a novel image-text matching loss termed as Cross-Modal Projec- tion Matching (CMPM), which incorporates the cross-modal projection into KL divergence to associate the representations across different modalities. Given a mini-batch with n image a...
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,...
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...
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, ...
几篇论文实现代码:《Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing》(ICLR 2024) GitHub: github.com/YangLing0818/ContextDiff [fig5] 《APISR: Anime Produc...
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...
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...
Independent random transform TvisTvis and TothToth would be conducted on raw visible and the other modal images for two parallel self-supervised learning. 2) Overall loss function: The input is processed by the network to generate the feature map, and then the descriptors and score maps are ...