Symmetry-Aware Transformer-Based Mirror Detection

نویسندگان

چکیده

Mirror detection aims to identify the mirror regions in given input image. Existing works mainly focus on integrating semantic features and structural mine specific relations between non-mirror regions, or introducing properties like depth chirality help analyze existence of mirrors. In this work, we observe that a real object typically forms loose symmetry relationship with its corresponding reflection mirror, which is beneficial distinguishing mirrors from objects. Based observation, propose dual-path Symmetry-Aware Transformer-based Network (SATNet), includes two novel modules: Attention Module (SAAM) Contrast Fusion Decoder (CFDM). Specifically, first adopt transformer backbone model global information aggregation images, extracting multi-scale paths. We then feed high-level SAAMs capture relations. Finally, fuse refine our prediction maps progressively CFDMs obtain final mask. Experimental results show SATNet outperforms both RGB RGB-D methods all available datasets.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2023

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v37i1.25173