{"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/icnet-intra-saliency-correlation-network-for","title":"ICNet: Intra-saliency Correlation Network for Co-Saliency Detection","arxiv_id":null,"date":"2020-12-01","proceeding":"NeurIPS 2020 12","authors":["Wen-Da Jin","Jun Xu","Ming-Ming Cheng","Yi Zhang","Wei Guo"],"abstract":"Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD). Model-based methods produce coarse Co-SOD results due to hand-crafted intra- and inter-saliency features. Current data-driven models exploit inter-saliency cues, but undervalue the potential power of intra-saliency cues. In this paper, we propose an Intra-saliency Correlation Network (ICNet) to extract intra-saliency cues from the single image saliency maps (SISMs) predicted by any off-the-shelf SOD method, and obtain inter-saliency cues by correlation techniques. Specifically, we adopt normalized masked average pooling (NMAP) to extract latent intra-saliency categories from the SISMs and semantic features as intra cues. Then we employ a correlation fusion module (CFM) to obtain inter cues by exploiting correlations between the intra cues and single-image features. To improve Co-SOD performance, we propose a category-independent rearranged self-correlation feature (RSCF) strategy. Experiments on three benchmarks show that our ICNet outperforms previous state-of-the-art methods on Co-SOD. Ablation studies validate the effectiveness of our contributions. The PyTorch code is available at https://github.com/blanclist/ICNet.","url_abs":"http://proceedings.neurips.cc/paper/2020/hash/d961e9f236177d65d21100592edb0769-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2020/file/d961e9f236177d65d21100592edb0769-Paper.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":"icnet-intra-saliency-correlation-network-for","repo_url":"https://github.com/blanclist/ICNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"icnet-intra-saliency-correlation-network-for","repo_url":"https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ICNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"icnet-intra-saliency-correlation-network-for","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/ICNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"icnet-intra-saliency-correlation-network-for","repo_url":"https://github.com/code-implementation1/Code4/tree/main/ICNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"saliency-detection","task_name":"Saliency Detection"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}