Coarse to Fine Multi-Resolution Temporal Convolutional Network

Understand

Temporal convolutional networks (TCNs) are a commonly used architecture for temporal video segmentation.

• TCNs however, tend to suffer from over-segmentation errors and require additional refinement modules to ensure smoothness and temporal coherency.
• In this work, we propose a novel temporal encoder-decoder to tackle the problem of sequence fragmentation.
• In particular, the decoder follows a coarse-to-fine structure with an implicit ensemble of multiple temporal resolutions.