Edge-Aware Graph Matching Network for Part-Based Semantic Segmentation
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Published:2022-09-03
Issue:11
Volume:130
Page:2797-2821
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ISSN:0920-5691
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Container-title:International Journal of Computer Vision
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language:en
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Short-container-title:Int J Comput Vis
Author:
Michieli Umberto, Zanuttigh PietroORCID
Abstract
AbstractSemantic segmentation of parts of objects is a marginally explored and challenging task in which multiple instances of objects and multiple parts within those objects must be recognized in an image. We introduce a novel approach (GMENet) for this task combining object-level context conditioning, part-level spatial relationships, and shape contour information. The first target is achieved by introducing a class-conditioning module that enforces class-level semantics when learning the part-level ones. Thus, intermediate-level features carry object-level prior to the decoding stage. To tackle part-level ambiguity and spatial relationships among parts we exploit an adjacency graph-based module that aims at matching the spatial relationships between parts in the ground truth and predicted maps. Last, we introduce an additional module to further leverage edges localization. Besides testing our framework on the already used Pascal-Part-58 and Pascal-Person-Part benchmarks, we further introduce two novel benchmarks for large-scale part parsing, i.e., a more challenging version of Pascal-Part with 108 classes and the ADE20K-Part benchmark with 544 parts. GMENet achieves state-of-the-art results in all the considered tasks and furthermore allows to improve object-level segmentation accuracy.
Funder
Università degli Studi di Padova
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Reference98 articles.
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., ... Zheng, X. (2016). Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI) (pp. 265–283). 2. Azizpour, H., & Laptev, I. (2012). Object detection using strongly-supervised deformable part models. In Proceedings of European conference on computer vision (ECCV) (pp. 836–849). Springer. 3. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 39(12), 2481–2495. 4. Cao, K., Wei, C., Gaidon, A., Arechiga, N., & Ma, T. (2019). Learning imbalanced datasets with label-distribution-aware margin loss. In Neural information processing systems (NeurIPS) (pp. 1567–1578). 5. Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E., & Caputo, B. (2020). Modeling the background for incremental learning in semantic segmentation. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR) (pp. 9233–9242).
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