VideoMatch: Matching based Video Object Segmentation
This addresses the inefficiency of real-time video analysis for applications requiring fast processing, though it is incremental as it builds on existing segmentation frameworks.
The paper tackles the problem of video object segmentation by proposing a matching-based algorithm that avoids the need for fine-tuning during test time, achieving comparable performance to state-of-the-art methods while significantly reducing computational time.
Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and computationally expensive, hence the algorithms are far from real time. To address this issue, we develop a novel matching based algorithm for video object segmentation. In contrast to memorization based classification techniques, the proposed approach learns to match extracted features to a provided template without memorizing the appearance of the objects. We validate the effectiveness and the robustness of the proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and JumpCut datasets. Extensive results show that our method achieves comparable performance without fine-tuning and is much more favorable in terms of computational time.