Title:《Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval》 Published:2021 ACM MM 我想做图文检索的人开始都会有一个比较直接的想法就是对图进行树的构建,因为,文本是有语法结构的可以构建语法树,而图像是连续空间,没有语法结构。如果图像也能够构建树得到一个对图像的语义理解表...
CDNeRF: A Multi-modal Feature Guided Neural Radiance FieldsWe present CDNeRF, a simple yet powerful learning framework that creates novel view synthesis by reconstructing neural radiance fields from a single view RGB image. Novel view synthesis by neural radiance fields has achieved great improvement...
具体来说,先分别对图像和文本提特征,这时图像对应生成 I1、I2 ... In 的特征向量(Image Feature),文本对应生成 T1、T2 ... Tn 的特征向量(Text Feature),中间对角线为正样本,其余均为负样本。 拷贝自CLIP:了解in-batch 正负样本构造 拷贝自CLIP:了解模型的结构 拷贝自CLIP:了解如何用CLIP做分类 Inference:...
4.3 Multi-modal Feature Fusing在本工作中,由于有三种类型的数据,我们采用了具有共同注意方法的层次融合模式[Lu et al.,2019]。为了捕获跨模态关系的不同方面并增强多模态特征,我们提出在自监督损失下强制执行跨模态对齐。Cross-modal Co-attention Mechanism...
A novel, multi-modal feature fusion based framework is prosed to obtain an effective representation for each superpixel annotation. The framework consists of four sequential modules (Fig. 2): 1) a double-channel (including both shallow and deep modality) based, low-level feature extraction; 2...
This is the official repository ofMM-Interleaved: an end-to-end generative model for interleaved image-text data. Introduction MM-Interleavedis a new end-to-end generative model for interleaved image-text modeling. It introduces a novel fine-grained multi-modal feature synchronizer namedMMFS, allowi...
More specifically, the TGANN model contains four parts: feature extraction, text-guided attention mechanism, feature fusion, and popularity prediction. For the feature extraction, we propose a filter-based topic model, an extension of latent Dirichlet allocation (LDA) (Blei et al., 2003), to ...
Our proposed Multi-Modal Transformer (MMT) aggregates sequences of multi-modal features (e.g. appearance, motion, audio, OCR, etc.) from a video. It then embeds the aggregated multi-modal feature to a shared space with text for retrieval. It achieves state-of-the-art performance on MSRVT...
(Positive Valence,Negative Valence,Difference in ValenceandArousal) related to the emotional state of the team. The featureDifference in Valenceis of interest as it immediately highlights that a team with a higher value has a positive emotional state. Please note that in this work, we do not ...
Here, we observe that all of the selected feature parameters x i (k) are correlated with the subjective scores with positive values (i.e., they are proportional to excitability). Also, in each correlation value in Table 2, the probability of the null hypothesis (p value) is p<0.05 (i...