Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex
video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex
Video Object Segmentation Track based on MOSE dataset and Motion Expression
guided Video Segmentation track based on MeViS dataset. In the two new tracks,
we provide additional videos and annotations that feature challenging elements,
such as the disappearance and reappearance of objects, inconspicuous small
objects, heavy occlusions, and crowded environments in MOSE. Moreover, we
provide a new motion expression guided video segmentation dataset MeViS to
study the natural language-guided video understanding in complex environments.
These new videos, sentences, and annotations enable us to foster the
development of a more comprehensive and robust pixel-level understanding of
video scenes in complex environments and realistic scenarios. The MOSE
challenge had 140 registered teams in total, 65 teams participated the
validation phase and 12 teams made valid submissions in the final challenge
phase. The MeViS challenge had 225 registered teams in total, 50 teams
participated the validation phase and 5 teams made valid submissions in the
final challenge phase.

复杂环境下像素级视频理解的挑战，提供了基于 MOSE 数据集的复杂视频对象分割以及基于 MeViS 数据集的运动表达引导的视频分割两个新的跟踪，并通过提供具有挑战性元素的额外视频和注释来促进像素级视频场景的综合和强大的理解。