Kernelized Memory Network for Video Object Segmentation
This work addresses a key limitation in semi-supervised video object segmentation for computer vision applications, offering a significant performance boost over existing methods.
The authors tackled the mismatch between non-local space-time memory networks and the predominantly local nature of video object segmentation by proposing a kernelized memory network (KMN), which achieved a 5% improvement on the DAVIS 2017 test-dev set and runs at 0.12 seconds per frame on DAVIS 2016.
Semi-supervised video object segmentation (VOS) is a task that involves predicting a target object in a video when the ground truth segmentation mask of the target object is given in the first frame. Recently, space-time memory networks (STM) have received significant attention as a promising solution for semi-supervised VOS. However, an important point is overlooked when applying STM to VOS. The solution (STM) is non-local, but the problem (VOS) is predominantly local. To solve the mismatch between STM and VOS, we propose a kernelized memory network (KMN). Before being trained on real videos, our KMN is pre-trained on static images, as in previous works. Unlike in previous works, we use the Hide-and-Seek strategy in pre-training to obtain the best possible results in handling occlusions and segment boundary extraction. The proposed KMN surpasses the state-of-the-art on standard benchmarks by a significant margin (+5% on DAVIS 2017 test-dev set). In addition, the runtime of KMN is 0.12 seconds per frame on the DAVIS 2016 validation set, and the KMN rarely requires extra computation, when compared with STM.