In:
Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 37, No. 1 ( 2023-06-26), p. 313-321
Kurzfassung:
Amodal instance segmentation aims to infer the amodal mask, including both the visible part and occluded part of each object instance. Predicting the occluded parts is challenging. Existing methods often produce incomplete amodal boxes and amodal masks, probably due to lacking visual evidences to expand the boxes and masks. To this end, we propose a prior-guided expansion framework, which builds on a two-stage segmentation model (i.e., Mask R-CNN) and performs box-level (resp., pixel-level) expansion for amodal box (resp., mask) prediction, by retrieving regression (resp., flow) transformations from a memory bank of expansion prior. We conduct extensive experiments on KINS, D2SA, and COCOA cls datasets, which show the effectiveness of our method.
Materialart:
Online-Ressource
ISSN:
2374-3468
,
2159-5399
DOI:
10.1609/aaai.v37i1.25104
Sprache:
Unbekannt
Verlag:
Association for the Advancement of Artificial Intelligence (AAAI)
Publikationsdatum:
2023
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