Image segmentation is the task of associating pixels in an image with their respective object
class labels. It has a wide range of applications in many industries including healthcare …

Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …

Recent progress in vision Transformers exhibits great success in various tasks driven by the
new spatial modeling mechanism based on dot-product self-attention. In this paper, we …

This work investigates a simple yet powerful adapter for Vision Transformer (ViT). Unlike
recent visual transformers that introduce vision-specific inductive biases into their …

Image segmentation groups pixels with different semantics, eg, category or instance
membership. Each choice of semantics defines a task. While only the semantics of each task …

We design an open-vocabulary image segmentation model to organize an image into
meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite …

Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …

We present a mask-piloted Transformer which improves masked-attention in Mask2Former
for image segmentation. The improvement is based on our observation that Mask2Former …

In standard hospital blood tests, the traditional process requires doctors to manually isolate
leukocytes from microscopic images of patients' blood using microscopes. These isolated …

Real-world applications have high demands for semantic segmentation methods. Although
semantic segmentation has made remarkable leap-forwards with deep learning, the …