(i) a novel Cross-Modal Feature Alignment (X-FA) loss, (ii) an attention-based Cross-Modal Feature Fusion (X-FF) module to align multi-modal BEV features implicitly, and (iii) an auxiliary PV segmentation branch with Cross-View Segmentation Alignment (X-SA) losses to improve the PV-to...
The dual-path alignment。(减小模态之间的差异) 两种对齐: text-to-visual(视觉特征),visual-to-text(多粒度语义表示)。 具体步骤: - 开始时,视频tokens输入过p全连接层,产生多样的clip-level的视觉特征V \in \mathbb{R}^{p \times n \times d },于多粒度句子表示的输入对应。接下来使用text-to-visual...
3.2 GNN-based language and image fusion for cross-modal fusion Cross-modal alignment is an essential step in referring image segmentation. Many current methods map language features to the whole image to obtain language vectors corresponding to all pixels. Then, they achieve cross-modal fusion by ...
在跨模态检索中,token embeddings alignment(标记嵌入对齐)是一个关键步骤,它旨在将来自不同模态(如图像和文本)的数据映射到同一潜在空间,以便能够比较它们的相似性。 标记嵌入对齐的重要性 跨模态相似性度量:跨模态检索的主要挑战之一是测量不同模态数据之间的相似性。标记嵌入对齐通过将不同模态的数据映射到同一空间,...
Cross-Modal Correlation Alignment (CMCA) CMCA方案是通过探索所有实例在不同模态下的总体数据分布来度量跨模态相关性的方案。 CMCA方案将所有真实例和合成实例的嵌入特征作为矩阵形式,用矩阵的协方差来度量跨模态距离。 协方差矩阵表示的是每个特征与其他特征之间的线性关系。对于图像和文本的嵌入特征可以认为是从 s 个...
Specifically, grounded in Optimal Transport, we introduce a local cross-modal alignment module that explicitly learns token-level correspondences between different modalities. Moreover, we propose a global cross-modal alignment loss based on Maximum Mean Discrepancy to implicitly enforce the consistency ...
Cross-modal alignment aims to build a bridge connecting vision and language. It is an important multi-modal task that efficiently learns the semantic similarities between images and texts. Traditional fine-grained alignment methods heavily rely on pre-trained object detectors to extract region features...
Cross-modal alignment with graph reasoning for image-text retrieval Article 18 March 2022 Flexible graph-based attention and pooling network for image-text retrieval Article 16 December 2023 References Chen Y, Huang R, Chang H, et al. Cross-modal knowledge adaptation for language-based person...
根据上述观察,作者通过 Generalized Zero-Shot Classification(GZSC)的Aligned Cross-Modal Representations(ACMR),提出了一种新的 VAE 网络。 整体概念图 创新点 提出了 ACMR,并在四个公开数据集上都取得了 SOTA 的性能 提出了一种新的 Vision-Semantic Alignment(VSA)方法,用于加强跨模态特征对齐 提出了一种新的 ...
Cross-modal retrieval aims to correlate multimedia data by bridging the heterogeneity gap. Most cross-modal retrieval approaches learn a common subspace to