Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …

Pre-training by numerous image data has become de-facto for robust 2D representations. In
contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders …

Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth
object to one prediction, for end-to-end detection without NMS post-processing. It is known …

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 …

Masked autoencoders are scalable vision learners, as the title of MAE\cite {he2022masked},
which suggests that self-supervised learning (SSL) in vision might undertake a similar …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

Recently, semantic segmentation models trained with image-level text supervision have
shown promising results in challenging open-world scenarios. However, these models still …