Categories: FAANG

4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities

*Equal Contributors
Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually rather small) number of modalities and tasks they are trained on. In this paper, we significantly expand upon the capabilities of 4M by training it on tens of highly diverse modalities and by performing co-training on large-scale multimodal datasets and text corpora. This includes training on several semantic and geometric modalities, feature maps from…

Robustness in Multimodal Learning under Train-Test Modality Mismatch

Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned with understanding how models behave as the type of modalities differ between training and deployment, a situation that naturally arises in many applications of multimodal learning to…

June 3, 2023

In "FAANG"

SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets

*= Equal Contributors We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises acquired and derived heterogeneous data modalities that we transform to semantically meaningful, image-like tensors to address the challenges of representation, alignment, and fusion of multimodal data. SeMAnD…

November 9, 2023

In "FAANG"