Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks
in 3D perception and modeling. However the remarkable accuracy of recent MMDE methods …

Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-
posedness of the single-image reconstruction problem, most well-established methods are …

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
(CLIP) have shown inspirational performance on 2D visual recognition, which learns to …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …