Projects | Unseen Object Segmentation in Videos via Transferable Representations

ACCV 2018 | We exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: i) solving a submodular function for selecting object-like segments and ii) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video.