{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-fourier-embedded-network-for-bi-modal","title":"Deep Fourier-embedded Network for Bi-modal Salient Object Detection","arxiv_id":"2411.18409","date":"2024-11-27","proceeding":null,"authors":["Pengfei Lyu","Xiaosheng Yu","Chengdong Wu","Jagath C. Rajapakse"],"abstract":"The rapid development of deep learning provides a significant improvement of salient object detection combining both RGB and thermal images. However, existing deep learning-based models suffer from two major shortcomings. First, the computation and memory demands of Transformer-based models with quadratic complexity are unbearable, especially in handling high-resolution bi-modal feature fusion. Second, even if learning converges to an ideal solution, there remains a frequency gap between the prediction and ground truth. Therefore, we propose a purely fast Fourier transform-based model, namely deep Fourier-embedded network (DFENet), for learning bi-modal information of RGB and thermal images. On one hand, fast Fourier transform efficiently fetches global dependencies with low complexity. Inspired by this, we design modal-coordinated perception attention to fuse the frequency gap between RGB and thermal modalities with multi-dimensional representation enhancement. To obtain reliable detailed information during decoding, we design the frequency-decomposed edge-aware module (FEM) to clarify object edges by deeply decomposing low-level features. Moreover, we equip proposed Fourier residual channel attention block in each decoder layer to prioritize high-frequency information while aligning channel global relationships. On the other hand, we propose co-focus frequency loss (CFL) to steer FEM towards minimizing the frequency gap. CFL dynamically weights hard frequencies during edge frequency reconstruction by cross-referencing the bi-modal edge information in the Fourier domain. This frequency-level refinement of edge features further contributes to the quality of the final pixel-level prediction. Extensive experiments on four bi-modal salient object detection benchmark datasets demonstrate our proposed DFENet outperforms twelve existing state-of-the-art models.","url_abs":"https://arxiv.org/abs/2411.18409v1","url_pdf":"https://arxiv.org/pdf/2411.18409v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-fourier-embedded-network-for-bi-modal","repo_url":"https://github.com/joshualpf/dfenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"fem","method_name":"FEM"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}