{"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/scene-context-aware-salient-object-detection","title":"Scene Context-Aware Salient Object Detection","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Avishek Siris","Jianbo Jiao","Gary K.L. Tam","Xianghua Xie","Rynson W.H. Lau"],"abstract":"    Salient object detection identifies objects in an image that grab visual attention. Although contextual features are considered in recent literature, they often fail in real-world complex scenarios. We observe that this is mainly due to two issues: First, most existing datasets consist of simple foregrounds and backgrounds that hardly represent real-life scenarios. Second, current methods only learn contextual features of salient objects, which are insufficient to model high-level semantics for saliency reasoning in complex scenes. To address these problems, we first construct a new large-scale dataset with complex scenes in this paper. We then propose a context-aware learning approach to explicitly exploit the semantic scene contexts. Specifically, two modules are proposed to achieve the goal: 1) a Semantic Scene Context Refinement module to enhance contextual features learned from salient objects with scene context, and 2) a Contextual Instance Transformer to learn contextual relations between objects and scene context. To our knowledge, such high-level semantic contextual information of image scenes is under-explored for saliency detection in the literature. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art techniques in complex scenarios for saliency detection, and transfers well to other existing datasets. The code and dataset are available at https://github.com/SirisAvishek/Scene_Context_Aware_Saliency.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Siris_Scene_Context-Aware_Salient_Object_Detection_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Siris_Scene_Context-Aware_Salient_Object_Detection_ICCV_2021_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":"scene-context-aware-salient-object-detection","repo_url":"https://github.com/sirisavishek/scene_context_aware_saliency","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"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}