{"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/d3g-exploring-gaussian-prior-for-temporal","title":"D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation","arxiv_id":"2308.04197","date":"2023-08-08","proceeding":"ICCV 2023 1","authors":["Hanjun Li","Xiujun Shu","Sunan He","Ruizhi Qiao","Wei Wen","Taian Guo","Bei Gan","Xing Sun"],"abstract":"Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a large performance gap compared to fully supervised ones, while the latter requires laborious timestamp annotations. In this study, we aim to reduce the annotation cost yet keep competitive performance for TSG task compared to fully supervised ones. To achieve this goal, we investigate a recently proposed glance-supervised temporal sentence grounding task, which requires only single frame annotation (referred to as glance annotation) for each query. Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples reliable positive moments from a 2D temporal map via jointly leveraging Gaussian prior and semantic consistency, which contributes to aligning the positive sentence-moment pairs in the joint embedding space. Moreover, to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events, we propose the DGA module, which adjusts the distribution dynamically to approximate the ground truth of target moments. Extensive experiments on three challenging benchmarks verify the effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods. Code is available at https://github.com/solicucu/D3G.","url_abs":"https://arxiv.org/abs/2308.04197v1","url_pdf":"https://arxiv.org/pdf/2308.04197v1.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":"d3g-exploring-gaussian-prior-for-temporal","repo_url":"https://github.com/solicucu/d3g","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"temporal-sentence-grounding","task_name":"Temporal Sentence Grounding"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-sentence-grounding-on-charades-sta","task":"Temporal Sentence Grounding","dataset":"Charades-STA","model":"D3G (Semi-weak, MViT-K400-Pretrain-feature, evaluated by AdaFocus)","rank_in_archive_order":11,"of":13,"metrics":{"R1@0.5":"46.0","R1@0.7":"20.2","R5@0.5":"83.1","R5@0.7":"50.2"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-sentence-grounding-on-charades-sta","task":"Temporal Sentence Grounding","dataset":"Charades-STA","model":"D3G (Semi-weak, I3D-K400-Pretrain-feature, evaluated by AdaFocus)","rank_in_archive_order":12,"of":13,"metrics":{"R1@0.5":"41.7","R1@0.7":"18.8","R5@0.5":"78.2","R5@0.7":"48.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.04197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04197"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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