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...
(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...
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 ...
In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities including 2D image, 3D point cloud and mesh data. 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) ...
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 ...
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...
Loss functions need to be modified when it comes to multi-modality transitions. Thus, a general-purpose strategy for medical modality transitions is of great significance. Fortunately, this is achieved by our cross-modality image generation framework. The previous version of our manuscript is ...
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 ...
考虑两个训练前任务:masked multi-modal modelling and multi-modal alignment prediction. 在两个训练任务下训练ViLBERT在概念标题数据集上学习视觉基础。在掩蔽多模态学习中,模型必须在给定的观测输入条件下,为掩蔽输入重构图像区域类别或单词。在多模态对齐预测中,模型必须预测标题是否描述了图像内容。