In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

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

Abstract Video Foundation Models (VFMs) have received limited exploration due to high
computational costs and data scarcity. Previous VFMs rely on Image Foundation Models …

We present a novel masked image modeling (MIM) approach, context autoencoder (CAE),
for self-supervised representation pretraining. We pretrain an encoder by making predictions …

Large vision-language models have achieved outstanding performance, but their size and
computational requirements make their deployment on resource-constrained devices and …

Recently, significant progress has been made in masked image modeling to catch up to
masked language modeling. However, unlike words in NLP, the lack of semantic …

Masked image modeling (MIM) has attracted much research attention due to its promising
potential for learning scalable visual representations. In typical approaches, models usually …

Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks
via randomly masking image patches and reconstruction. However, effective data …

There has been significant progress in Masked Image Modeling (MIM). Existing MIM
methods can be broadly categorized into two groups based on the reconstruction target …

Abstract Vision Transformers (ViTs) emerge to achieve impressive performance on many
data-abundant computer vision tasks by capturing long-range dependencies among local …