  The referring video object segmentation task (RVOS) aims to segment object
instances in a given video referred by a language expression in all video
frames. Due to the requirement of understanding cross-modal semantics within
individual instances, this task is more challenging than the traditional
semi-supervised video object segmentation where the ground truth object masks
in the first frame are given. With the great achievement of Transformer in
object detection and object segmentation, RVOS has been made remarkable
progress where ReferFormer achieved the state-of-the-art performance. In this
work, based on the strong baseline framework--ReferFormer, we propose several
tricks to boost further, including cyclical learning rates, semi-supervised
approach, and test-time augmentation inference. The improved ReferFormer ranks
2nd place on CVPR2022 Referring Youtube-VOS Challenge.
