This paper presents a method to co-segment an object from wide baseline multiview images using cross-view self-supervision. A key challenge in the wide baseline images lies in the fragility of photometric matching. Inspired by shape-from-silhouette that does not require photometric matching, we formulate a new theory of shape belief transfer - the segmentation belief in one image can be used to predict that of the other image through epipolar geometry. This formulation is differentiable, and therefore, an end-to-end training is possible. We analyze the shape belief transfer to identify the theoretical upper and lower bounds of the unlabeled data segmentation, which characterizes the degenerate cases of co-segmentation. We design a novel triple network that embeds this shape belief transfer, which is agnostic to visual appearance and baseline. The resulting network is validated by recognizing a target object from realworld visual data including non-human species and a subject of interest in social videos where attaining large-scale annotated data is challenging.
|Original language||English (US)|
|Title of host publication||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Mar 2020|
|Event||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States|
Duration: Mar 1 2020 → Mar 5 2020
|Name||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
|Conference||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020|
|Period||3/1/20 → 3/5/20|
Bibliographical noteFunding Information:
This work is supported by NSF IIS 1846031 and NSF IIS 1755895.
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