{"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/saliency-detection-by-forward-and-backward","title":"Saliency Detection by Forward and Backward Cues in Deep-CNNs","arxiv_id":"1703.00152","date":"2017-03-01","proceeding":null,"authors":["Nevrez Imamoglu","Chi Zhang","Wataru Shimoda","Yuming Fang","Boxin Shi"],"abstract":"As prior knowledge of objects or object features helps us make relations for\nsimilar objects on attentional tasks, pre-trained deep convolutional neural\nnetworks (CNNs) can be used to detect salient objects on images regardless of\nthe object class is in the network knowledge or not. In this paper, we propose\na top-down saliency model using CNN, a weakly supervised CNN model trained for\n1000 object labelling task from RGB images. The model detects attentive regions\nbased on their objectness scores predicted by selected features from CNNs. To\nestimate the salient objects effectively, we combine both forward and backward\nfeatures, while demonstrating that partially-guided backpropagation will\nprovide sufficient information for selecting the features from forward run of\nCNN model. Finally, these top-down cues are enhanced with a state-of-the-art\nbottom-up model as complementing the overall saliency. As the proposed model is\nan effective integration of forward and backward cues through objectness\nwithout any supervision or regression to ground truth data, it gives promising\nresults compared to state-of-the-art models in two different datasets.","url_abs":"http://arxiv.org/abs/1703.00152v2","url_pdf":"http://arxiv.org/pdf/1703.00152v2.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":"saliency-detection-by-forward-and-backward","repo_url":"https://github.com/nevrez/Saliency_BackwardForwardFeatures_VGG16","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"}],"methods":[],"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}