  Video segmentation requires consistently segmenting and tracking objects over
time. Due to the quadratic dependency on input size, directly applying
self-attention to video segmentation with high-resolution input features poses
significant challenges, often leading to insufficient GPU memory capacity.
Consequently, modern video segmenters either extend an image segmenter without
incorporating any temporal attention or resort to window space-time attention
in a naive manner. In this work, we present Axial-VS, a general and simple
framework that enhances video segmenters by tracking objects along axial
trajectories. The framework tackles video segmentation through two sub-tasks:
short-term within-clip segmentation and long-term cross-clip tracking. In the
first step, Axial-VS augments an off-the-shelf clip-level video segmenter with
the proposed axial-trajectory attention, sequentially tracking objects along
the height- and width-trajectories within a clip, thereby enhancing temporal
consistency by capturing motion trajectories. The axial decomposition
significantly reduces the computational complexity for dense features, and
outperforms the window space-time attention in segmentation quality. In the
second step, we further employ axial-trajectory attention to the object queries
in clip-level segmenters, which are learned to encode object information,
thereby aiding object tracking across different clips and achieving consistent
segmentation throughout the video. Without bells and whistles, Axial-VS
showcases state-of-the-art results on video segmentation benchmarks,
emphasizing its effectiveness in addressing the limitations of modern
clip-level video segmenters. Code and models are available at
https://github.com/TACJu/Axial-VS.
