Papers › Deep Transport Network for Unsupervised Video Object Segmentation
Deep Transport Network for Unsupervised Video Object Segmentation
Kaihua Zhang, Zicheng Zhao, Dong Liu, Qingshan Liu, Bo Liu
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network. However, they cannot handle the distracting noises in each input modality, which may vastly deteriorate the model performance. We propose to establish the correspondence between the input modalities while suppressing the distracting signals via optimal structural matching. Given a video frame, we extract the dense local features from the RGB image and optical flow, and treat them as two complex structured representations. The Wasserstein distance is then employed to compute the global optimal flows to transport the features in one modality to the other, where the magnitude of each flow measures the extent of the alignment between two local features. To plug the structural matching into a two-stream network for end-to-end training, we factorize the input cost matrix into small spatial blocks and design a differentiable long-short Sinkhorn module consisting of a long-distant Sinkhorn layer and a short-distant Sinkhorn layer. We integrate the module into a dedicated two-stream network and dub our model TransportNet. Our experiments show that aligning motion-appearance yields the state-of-the-art results on the popular video object segmentation datasets.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Video Object Segmentation | DAVIS 2016 val | TransportNet | F | 85.0 | #13 of 25 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2016 val | TransportNet | G | 84.8 | #13 of 25 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2016 val | TransportNet | J | 84.5 | #13 of 25 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | FBMS test | TransportNet | J | 78.7 | #8 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections