{"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/tased-net-temporally-aggregating-spatial","title":"TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection","arxiv_id":"1908.05786","date":"2019-08-15","proceeding":"ICCV 2019 10","authors":["Kyle Min","Jason J. Corso"],"abstract":"TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information. As a result, a single prediction map is produced from an input clip of multiple frames. Frame-wise saliency maps can be predicted by applying TASED-Net in a sliding-window fashion to a video. The proposed approach assumes that the saliency map of any frame can be predicted by considering a limited number of past frames. The results of our extensive experiments on video saliency detection validate this assumption and demonstrate that our fully-convolutional model with temporal aggregation method is effective. TASED-Net significantly outperforms previous state-of-the-art approaches on all three major large-scale datasets of video saliency detection: DHF1K, Hollywood2, and UCFSports. After analyzing the results qualitatively, we observe that our model is especially better at attending to salient moving objects.","url_abs":"https://arxiv.org/abs/1908.05786v1","url_pdf":"https://arxiv.org/pdf/1908.05786v1.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":"tased-net-temporally-aggregating-spatial","repo_url":"https://github.com/kylemin/TASED-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"video-saliency-detection","task_name":"Video Saliency Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-saliency-detection-on-dhf1k","task":"Video Saliency Detection","dataset":"DHF1K","model":"TASED-Net","rank_in_archive_order":3,"of":3,"metrics":{"NSS":"2.667"},"uses_additional_data":false},{"leaderboard":"/sota/video-saliency-detection-on-msu-video","task":"Video Saliency Detection","dataset":"MSU Video Saliency Prediction","model":"TASED-Net","rank_in_archive_order":3,"of":14,"metrics":{"AUC-J":"0.852","CC":"0.710","FPS":"1.85","KLDiv":"0.538","NSS":"1.96","SIM":"0.610"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1908.05786","atlas_url":"https://app.syntology.ai/?focus=1908.05786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.05786"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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