In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

Current universal segmentation methods demonstrate strong capabilities in pixel-level
image and video understanding. However, they lack reasoning abilities and cannot be …

In this work, for the first time, we demonstrate that Mamba-based point cloud methods can
outperform point-based methods. Mamba exhibits strong global modeling capabilities and …

In-context segmentation has drawn more attention with the introduction of vision foundation
models. Most existing approaches adopt metric learning or masked image modeling to build …

Understanding the real world through point cloud video is a crucial aspect of robotics and
autonomous driving systems. However, prevailing methods for 4D point cloud recognition …

Contrastive learning has achieved great success in skeleton-based representation learning
recently. However, the prevailing methods are predominantly negative-based, necessitating …

Self-supervised learning has proved effective for skeleton-based human action
understanding. However, previous works either rely on contrastive learning that suffers false …

With the emergence of large-scale models trained on diverse datasets, in-context learning
has emerged as a promising paradigm for multitasking, notably in natural language …

Skeleton-based multi-entity action recognition is a challenging task aiming to identify
interactive actions or group activities involving multiple diverse entities. Existing models for …

The fundamental task of zero-shot skeleton-based action recognition is to learn existing
skeletal actions during the training phase and to accurately identify unseen actions during …