CONDA: Condensed Deep Association Learning for Co-salient Object Detection
Li, Long Liu, Nian Zhang, Dingwen Li, Zhongyu Khan, Salman Anwer, Rao Cholakkal, Hisham Han, Junwei Khan, Fahad Shahbaz
Li, Long
Liu, Nian
Zhang, Dingwen
Li, Zhongyu
Khan, Salman
Anwer, Rao
Cholakkal, Hisham
Han, Junwei
Khan, Fahad Shahbaz
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image association modeling. Because most of them focus on image feature optimization under the guidance of heuristically calculated raw inter-image associations. They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling. To alleviate these limitations, this paper proposes a deep association learning strategy that deploys deep networks on raw associations to explicitly transform them into deep association features. Specifically, we first create hyperassociations to collect dense pixel-pair-wise raw associations and then deploys deep aggregation networks on them. We design a progressive association generation module for this purpose with additional enhancement of the hyperassociation calculation. More importantly, we propose a correspondence-induced association condensation module that introduces a pretext task, i.e. semantic correspondence estimation, to condense the hyperassociations for computational burden reduction and noise elimination. We also design an object-aware cycle consistency loss for high-quality correspondence estimations. Experimental results in three benchmark datasets demonstrate the remarkable effectiveness of our proposed method with various training settings. The code is available at: https://github.com/dragonlee258079/CONDA.
Citation
L. Li et al., “CONDA: Condensed Deep Association Learning for Co-salient Object Detection,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15108 LNCS, pp. 287–303, 2025, doi: 10.1007/978-3-031-72973-7_17.
Source
Computer Vision – ECCV 2024
Conference
Keywords
Co-salient Object Detection, Deep Association Learning
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Source
Publisher
Springer Nature