GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector

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GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Title:
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
05 April 2023
Citation:
Zheng, P., Fu, H., Fan, D.-P., Fan, Q., Qin, J., Tai, Y.-W., Tang, C.-K., & Van Gool, L. (2023). GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–18. https://doi.org/10.1109/tpami.2023.3264571
Abstract:
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Central Research Fund
Grant Reference no. : NA

This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C222812010

This work is also supported by the National Natural Science Foundation of China (No. 62276129).
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2160-9292
1939-3539
0162-8828
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