模型具体结构如图: 2.1 Modality Representation Learning 2.1.1 Utterance-level Representations 每一个模态 都提取出序列特征, 我们把这个seq 通过一个LSTM, 并且LSTM的 最后一个隐层接一个全连接映射到同一维度 2.1.2 Modality-Invariant and -Specific Representations 我们把同一个特征 映射到两个不同的特征空间中...
阅读笔记 MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis,程序员大本营,技术文章内容聚合第一站。
The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the ...
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis - declare-lab/MISA
And meanwhile, multiple heads of classifiers with the improved part-aware BNNeck are integrated to supervise the network producing identity-discriminant representations w.r.t. both local details and global structures in the learned modality-invariant space. By training in an end-to-end manner, the ...
2. The guiding effect of self-supervision in the training process is rather beneficial when learning modality-invariant feature representations. Conclusion: In this article, we propose S2-Net, which introduces the self-supervised learning in the training learn the modality-invariant feature ...
Learning Modality-Specific Representations for Visible-Infrared Person Re-Identification. Z.Feng,J.Lai,X.Xie - 《IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society》 - 2019 - 被引量...
Fig. 28. Factorized Representations. Bousmalis et al. [86] addressed the domain adaptation problem by learning domain-invariant representations common to both domains and mutually orthogonal-specific representations unique to the modalities. Tsai et al. [87] proposed a discriminative-generative model th...
The rotationally equivariant CoMIRs together with invariant feature extractors like SIFT and SURF can handle the displacements between the images, and the representations suffice to bridge between the modalities of SHG and BF. The best results for cross-modality retrieval of transformed full-sized image...
modalities in such data provides supervision for disentangling the underlying explanatory factors of eachmodality...Previous work leveraging multimodal data has mainly focused on retaining only themodality-invariant...objectives to learn disentangled representations, which encode not only the shared factors,...