Transformer, one of the latest technological advances of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear …
In this work, we present SEEM, a promotable and interactive model for segmenting everything everywhere all at once in an image. In SEEM, we propose a novel and versatile …
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 …
Abstract This work presents Depth Anything a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules we aim to build a simple yet …
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of se-mantic …
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single- modality-dominated remote sensing (RS) applications, especially with an emphasis on …
Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation …
Q Yu, J He, X Deng, X Shen… - Advances in Neural …, 2024 - proceedings.neurips.cc
Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories in diverse environments. One way to address this …
Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation include scene parsing, panoptic segmentation, and, more recently, new …