Abstract
One-Shot Video Object Segmentation (OSVOS) is a CNN architecture to tackle the problem of semi-supervised video object segmentation, which performs the separation of an object from the background in a frame-independent way with the aid of one manually-segmented frame. However, in the scenarios of real applications, the requirement of one manually-segmented frame would do harm to user-friendliness of a system. To tackle the problem above, we propose a video object segmentation based on referring expression (named as REVOS), which obtains the segmented frame by a referring expression (a noun phrase whose function is to identify on specific object). The main task of our method is to select the target from all candidate objects which have the highest matching score with the referring expression by using the language analysis module. Then generate the annotation of the first frame and continue to segment all the remaining frames with OSVOS. The results of experiment show that our method can achieve similar accuracy to OSVOS and more convenient and flexible for system design.
The first author of this paper is a student. This work was supported by National Natural Science Foundation of China (No. 61373104, No. 61405143) and the Excellent Science and Technology Enterprise Specialist Project of Tianjin (No. 18JCTPJC59000) and the Tianjin Natural Science Foundation (No. 16JCYBJC42300).
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Bu, X., Wang, J., Liang, J., Liu, K., Sun, Y., Jin, G. (2019). One-Shot Video Object Segmentation Initialized with Referring Expression. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_35
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