The KITTI Vision Benchmark Suite

"Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration":
First, we leverage the complementary representations from unsupervised depth estimation and optical flow to produce effective pseudo-labels for training deep networks and predicting high-quality instance masks. Second, to enhance the mask generation, we devise a puzzle loss, which enables end-to-end training using box-level annotations. Third, our tracking module jointly utilizes bounding-box diagonal points with spatio-temporal discrepancy to model movements, which largely improves the robustness to different object appearances. Finally, our framework is flexible and enables image-level instance segmentation methods to operate the video-level task.

(Parameters for Best HOTA on "Car")

@article{STC-Seg,
title={Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration},
author={Liqi, Yan and Qifan, Wang and Siqi, Ma and Jingang, Wang and Changbin Yu},
journal={IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
year={2022},
}