模型包含三个 single-modal encoders, a cross-modal encoder, two cross-modal decoders. 跨模态理解需要对齐输入,从细粒度符号级别 到 粗粒度样本级别。工作的方式就是跨模态的翻译,cross-modal generation, 可以进行模态级别的建模。 所以作者提出的 OPT 模型可以从三个角度进行学习,即:token-level,modality-leve...
模型包含三个 single-modal encoders, a cross-modal encoder, two cross-modal decoders. 跨模态理解需要对齐输入,从细粒度符号级别 到 粗粒度样本级别。工作的方式就是跨模态的翻译,cross-modal generation, 可以进行模态级别的建模。 所以作者提出的 OPT 模型可以从三个角度进行学习,即:token-level,modality-leve...
Audio Encoder:采用wav2vec抽取特征,并输入LN。 Cross-Modal Encoder 三个独立模态的输出信息直接进行concat(在序列维度上进行concat),作为Cross-Modal Encoder的输入。 Cross-Modal Decoders Text/Visiondecoder负责文本/图像的重建,并通过其完成对应的下游任务。Text Decoder采用了类似Transformer decoder的结构。Vision De...
Cross-modal generationBackground With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, ...
(ArXiv'22) UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling MultiModal Pretraining 视频预训练里面,基本模态就是raw video, text information (title/caption/tag/subtitle等),audio information。粗略分为以下两类:Video-Text Pretraining和Video-Audio Pretraining,其实这两类也有...
Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one...
几篇论文实现代码:《Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing》(ICLR 2024) GitHub: github.com/YangLing0818/ContextDiff [fig5] 《APISR: Anime Produc...
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructed in an encoder-decoder framework, including three single-modal encoders to generate token-based embeddings for each ...
《Retrieving Multimodal Information for Augmented Generation: A Survey》是一篇由新加坡南洋理工大学、...
现在，我们有一种特殊的技巧，叫做 "检索-增强生成"（Retrieval-Augmented Generation），简称 RAG。