Deep kernel video approximation for unsupervised action segmentation
This work focuses on per-video unsupervised action segmentation, which is of interest to applications where storing large datasets is either not possible, or nor permitted. We propose to segment videos by learning in deep kernel space, to approximate the underlying frame distribution, as closely as possible. To define this closeness metric between the original video distribution and its approximation, we rely on maximum mean discrepancy (MMD) which is a geometry-preserving metric in distribution space, and thus gives more reliable estimates. Moreover, unlike the commonly used optimal transport metric, MMD is both easier to optimize, and faster. We choose to use neural tangent kernels (NTK...
| Authors | Silvia L. Pintea, Jouke Dijkstra |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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