CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917799757086720 |
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| author | Li, Long Liu, Nian Zhang, Dingwen Li, Zhongyu Khan, Salman Anwer, Rao Cholakkal, Hisham Han, Junwei Khan, Fahad Shahbaz |
| author_facet | Li, Long Liu, Nian Zhang, Dingwen Li, Zhongyu Khan, Salman Anwer, Rao Cholakkal, Hisham Han, Junwei Khan, Fahad Shahbaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Computer Vision and Pattern Recognition 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. |
| title | CONDA: Condensed Deep Association Learning for Co-Salient Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.01021 |