CrossModel Alignment CrossModel Alignment 目前的研究主要都是encode句子和视频片段为无结构的全局表示,用于多模态交互和融合。 本文提出的方法是SVMR(semantics-enriched video moment retrieval method)。能够清楚的获取分级多粒度的语义信息。 利用start and end time-aware filter kernels with visual cues去完成VMR...
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 ...
Finally, a two-stream feature alignment detector (TSFADet) based on the TSRA module is constructed for RGB-IR object detection in aerial images. With comprehensive experiments on the public DroneVehicle datasets, we verify that our method reduces the effect of the cross-modal misalignment and ...
an alignment module is designed to fuse multi-prompts (i.e., explicit and implicit ones) progressively and mitigate the cross-modal gap. Extensive experiments on the existing attribute-involved ReID datasets, namely, Market1501 and DukeMTMC-reID, demonstrate the effectiveness and rationality of the...
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) ...
In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB,...
Moreover, CMOT does not need paired multi-modal data for alignment. We found that not only does CMOT outperform existing state-of-art methods, but its inferred gene expression is biologically interpretable by evaluating on emerging single-cell multi-omics datasets. Finally, CMOT is open source ...
Annotation Efficient Cross-Modal Retrieval with Adversarial Attentive Alignment (MM2019) Cross-Modal Retrieval 主要问题:image-text的注释平行语料库难以获得,或代价高昂。如何在使用尽可能少的注释对语料库的情况下来进行有效的跨模态检索任务。 主要挑战:人工注释文本与机器生成的注释 [论文笔记]多模态数据集预处理...
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 发布于 2023-04-02 21:49・IP 属地北京 ...
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...