{"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/revisiting-video-saliency-a-large-scale","title":"Revisiting Video Saliency: A Large-scale Benchmark and a New Model","arxiv_id":"1801.07424","date":"2018-01-23","proceeding":"CVPR 2018 6","authors":["Wenguan Wang","Jianbing Shen","Fang Guo","Ming-Ming Cheng","Ali Borji"],"abstract":"In this work, we contribute to video saliency research in two ways. First, we\nintroduce a new benchmark for predicting human eye movements during dynamic\nscene free-viewing, which is long-time urged in this field. Our dataset, named\nDHF1K (Dynamic Human Fixation), consists of 1K high-quality, elaborately\nselected video sequences spanning a large range of scenes, motions, object\ntypes and background complexity. Existing video saliency datasets lack variety\nand generality of common dynamic scenes and fall short in covering challenging\nsituations in unconstrained environments. In contrast, DHF1K makes a\nsignificant leap in terms of scalability, diversity and difficulty, and is\nexpected to boost video saliency modeling. Second, we propose a novel video\nsaliency model that augments the CNN-LSTM network architecture with an\nattention mechanism to enable fast, end-to-end saliency learning. The attention\nmechanism explicitly encodes static saliency information, thus allowing LSTM to\nfocus on learning more flexible temporal saliency representation across\nsuccessive frames. Such a design fully leverages existing large-scale static\nfixation datasets, avoids overfitting, and significantly improves training\nefficiency and testing performance. We thoroughly examine the performance of\nour model, with respect to state-of-the-art saliency models, on three\nlarge-scale datasets (i.e., DHF1K, Hollywood2, UCF sports). Experimental\nresults over more than 1.2K testing videos containing 400K frames demonstrate\nthat our model outperforms other competitors.","url_abs":"http://arxiv.org/abs/1801.07424v3","url_pdf":"http://arxiv.org/pdf/1801.07424v3.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":"revisiting-video-saliency-a-large-scale","repo_url":"https://github.com/wenguanwang/DHF1K","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"video-saliency-detection","task_name":"Video Saliency Detection"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[{"slug":"dhf1k","name":"DHF1K","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-saliency-detection-on-msu-video","task":"Video Saliency Detection","dataset":"MSU Video Saliency Prediction","model":"ACLNet","rank_in_archive_order":8,"of":14,"metrics":{"AUC-J":"0.839","CC":"0.651","FPS":"4.18","KLDiv":"0.593","NSS":"1.71","SIM":"0.586"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.07424","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}