1. Designed a self-learning framework to segment objects in unseen video using Python and Caffe
2. Achieved favorable performance against the state-of-the-art algorithms (Our method: average IoU 72.6% on DAVIS 2016 dataset; TransferNet: average IoU 62.4% on the same dataset)
3. Published paper as the third author: “Unseen Object Segmentation in Videos via Transferable Representations”, Asian Conference on Computer Vision (ACCV), 2018