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 …

Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

We present DySample, an ultra-lightweight and effective dynamic upsampler. While
impressive performance gains have been witnessed from recent kernel-based dynamic …

Training data for video segmentation are expensive to annotate. This impedes extensions of
end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Open-world object detection, as a more general and challenging goal, aims to recognize
and localize objects described by arbitrary category names. The recent work GLIP …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

Recent advancements in deep neural networks have made remarkable leap-forwards in
dense image prediction. However, the issue of feature alignment remains as neglected by …

Point clouds and RGB images are two general perceptional sources in autonomous driving.
The former can provide accurate localization of objects, and the latter is denser and richer in …