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...
核心思想是multi-grained alignment和data-dependent label smoothing。前者一般是通过标注框+GIOU loss得到(难以大量获取),后者需要依赖一个预训练模型。作者把两者结合起来,算是一个比较高效的方式,思路也不难获得,主要效果取决于预训练模型的性能
Cross-modal AlignmentMasking on frequencyPrototypesData augmentationWe introduce a Cascaded Cross-modal Alignment framework for VI-ReID.We design a Channel-Spatial Recombination strategy to reduce discrepancies in inputs.We propose a frequency-level Low Frequency Masking module to enhance global details.We...
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 ...
However, clientdata are often heterogeneous in real-world scenarios, and we observe that localtraining on heterogeneous client data would distort the multimodal representationlearning and lead to biased cross-modal alignment. To address this challenge,we propose a Federated Align as IDeal (FedAID) ...
Therefore, we propose BCRA: a bidirectional cross modal implicit relationship inference and alignment framework, introducing MIM as a supplement to MLM tasks. Firstly, we integrate the tasks of MIM and MLM. Building upon this foundation, in order to enhance multimodal interaction, we further ...
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...
We also design a Cross-Modal Distribution Alignment (CMDA) module to align the distributions of image and text representations. During training, the trainable parameters are denoted in orange and the encoders of CLIP are frozen. J: the number of augmentati...
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 ...
在本文中,我们首先提出了用于CLIP的多模态对齐提示(Multi-modal Alignment Prompt, MmAP),该指令在微调过程中对文本和视觉模态进行对齐。在MmAP的基础上,我们开发了一个创新的多任务提示学习框架。一方面,为了最大限度地提高高相似度任务的互补性,我们采用梯度驱动的任务分组方法,将任务划分为几个不相交的组,并为每个...