The “deep learning” era (2010s until …),促使多模态研究发展的关键促成因素有4个,1)新的大规模多模态数据集,2)GPU快速计算,3)强大的视觉特征抽取能力,4)强大的语言特征抽取能力。 表示学习三篇参考文献 Multimodal Deep Learning [ICML 2011] Multimodal Learning with Deep Boltzmann Machines [NIPS 2012] ...
作者研究了一种单塔设置,所有模式共享一个模型,如下图所示:但他设计提供了更高的通用性和扩展性,以及跨模式和跨任务知识转移的潜力。 Multimodal contrastive learning 给定n对图像文本对\lbrace (\textbf{i}_j,\textbf{t}_j) \rbrace _{j=1}^n,模型学习表征\mathcal{Z}_n=\lbrace ( \textbf{z}_{\te...
learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and...
Deep learning-based approaches have recently emerged to address these points by deriving nonlinear cell embeddings. Here we present contrastive learning of cell representations, Concerto, which leverages a self-supervised distillation framework to model multimodal single-cell atlases. Simply by discriminating...
3. Multi-modal Contrastive Learning: Noise-ContrastiveEstimation (NCE): 对于video-audio pairs,构建 pos 和 neg 的样本对,通过最小化负样本之间的相似性和最大化正样本对之间的相似度进行特征对齐; Multiple-Instance-Learning-NCE (MIL-NCE): 对于video-text pairs,利用 MIL-NCE 进行学习,即:比对一个视频的...
对于 350M 参数模型,使用 FSDP+AC 可以实现比 DDP 高 3 倍的批尺寸,对于 900M 参数模型,可以实现 5.5 倍的批尺寸。即使是 10B,最大的批尺寸也约是 20,这相当不错。FSDP+AC 基本上可以用较少的 GPU 实现较大的全局批尺寸,对对比学习任务 (contrastive learning task) 特别有效。
We formulate this intuition into a contrastive learning objective. Figure 3 graphically depicts the idea where the modality-specific encoders \(E\) extract embeddings from each modality. These are then mapped to the common motion pattern space \(\mathcal{O}_{m}\) through the motion pattern...
3. Multi-modal Contrastive Learning: Noise-ContrastiveEstimation (NCE): 对于video-audio pairs,构建 pos 和 neg 的样本对,通过最小化负样本之间的相似性和最大化正样本对之间的相似度进行特征对齐; Multiple-Instance-Learning-NCE (MIL-NCE): 对于video-text pairs,利用 MIL-NCE 进行学习,即:比对一个视频的...
In this study, we propose mclSTExp: a multimodal deep learning approach utilizing Transformer and contrastive learning architecture. Inspired by the field of natural language processing, we regard the spots detected by ST technology as ''words'' and the sequences of these spots as ''sentences''...
对于 350M 参数模型,使用 FSDP+AC 可以实现比 DDP 高 3 倍的批尺寸,对于 900M 参数模型,可以实现 5.5 倍的批尺寸。即使是 10B,最大的批尺寸也约是 20,这相当不错。FSDP+AC 基本上可以用较少的 GPU 实现较大的全局批尺寸,对对比学习任务 (contrastive learning task) 特别有效。