The KITTI Vision Benchmark Suite

TiMer (Time-Travel Mask Transformer) builds on top
of mask transformers for video segmentation by
directly predicting class-labeled tubes. A tube
contains segmentation masks linked along the time
axis. It is a clip-level pipeline that takes clip-
level frames and outputs per-clip results. We use
the clip length T = 2 to make it near online
method. To infer an entire video sequence, we use
simple tube IoU-based ID propagation to stitch the
clip-level results.

The clip length is set to 2. Runtime is not
measured.

@inproceedings{kim2022tubeformer,
title={TubeFormer-DeepLab: Video Mask
Transformer},
author={Kim, Dahun and Xie, Jun and Wang, Huiyu
and Qiao, Siyuan and Yu, Qihang and Kim, Hong-Seok
and Adam, Hartwig and Kweon, In So and Chen,
Liang-Chieh},
booktitle={Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition},
pages={13914--13924},
year={2022}
}