Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning

The existing unsupervised video object segmentation methods depend heavily on the segmentation model trained offline on a labeled training video set, and cannot well generalize to the test videos from a different domain with possible distribution shifts. We propose to perform online fine-tuning on the pre-trained segmentation model to adapt to any ad-hoc videos at the test time. To achieve this, we design an offline semi-supervised adversarial training process, which leverages the unlabeled video frames to improve the model generalizability while aligning the features of the labeled video frames with the features of the unlabeled video frames. With the trained segmentation model, we further conduct an online self-supervised adversarial finetuning, in which a teacher model and a student model are first initialized with the pre-trained segmentation model weights, and the pseudo label produced by the teacher model is used to supervise the student model in an adversarial learning framework. Through online finetuning, the student model is progressively updated according to the emerging patterns in each test video, which significantly reduces the test-time domain gap. We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST). The experiments show that our method outperforms the state-of-the-arts with significant gains on the popular video object segmentation datasets.