Learning Hierarchical Embedding for Video Instance Segmentation
Zheyun Qin, Xiankai Lu, Xiushan Nie, Xiantong Zhen, Yilong Yin
Abstract
In this paper, we address video instance segmentation using a new generative model that learns effective representations of the target and background appearance. We propose to exploit hierarchical structural embedding over spatio-temporal space, which is compact, powerful, and flexible in contrast to current tracking-by-detection methods. Specifically, our model segments and tracks instances across space and time in a single forward pass, which is formulated as hierarchical embedding learning. The model is trained to locate the pixels belonging to specific instances over a video clip. We firstly take advantage of a novel mixing function to better fuse spatio-temporal embeddings. Moreover, we introduce normalizing flows to further improve the robustness of the learned appearance embedding, which theoretically extends conventional generative flows to a factorized conditional scheme. Comprehensive experiments on the video instance segmentation benchmark, i.e., YouTube-VIS, demonstrate the effectiveness of the proposed approach. Furthermore, we evaluate our method on an unsupervised video object segmentation dataset to demonstrate its generalizability.