{"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/deeply-interleaved-two-stream-encoder-for","title":"Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation","arxiv_id":"2203.15969","date":"2022-03-30","proceeding":null,"authors":["Guang Feng","Lihe Zhang","Zhiwei Hu","Huchuan Lu"],"abstract":"Referring video segmentation aims to segment the corresponding video object described by the language expression. To address this task, we first design a two-stream encoder to extract CNN-based visual features and transformer-based linguistic features hierarchically, and a vision-language mutual guidance (VLMG) module is inserted into the encoder multiple times to promote the hierarchical and progressive fusion of multi-modal features. Compared with the existing multi-modal fusion methods, this two-stream encoder takes into account the multi-granularity linguistic context, and realizes the deep interleaving between modalities with the help of VLGM. In order to promote the temporal alignment between frames, we further propose a language-guided multi-scale dynamic filtering (LMDF) module to strengthen the temporal coherence, which uses the language-guided spatial-temporal features to generate a set of position-specific dynamic filters to more flexibly and effectively update the feature of current frame. Extensive experiments on four datasets verify the effectiveness of the proposed model.","url_abs":"https://arxiv.org/abs/2203.15969v1","url_pdf":"https://arxiv.org/pdf/2203.15969v1.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":[],"tasks":[{"task_slug":"referring-expression-segmentation","task_name":"Referring Expression Segmentation"},{"task_slug":"video-segmentation","task_name":"Video Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/referring-expression-segmentation-on-a2d","task":"Referring Expression Segmentation","dataset":"A2D Sentences","model":"VLIDE","rank_in_archive_order":6,"of":27,"metrics":{"AP":"0.469","IoU mean":"0.598","IoU overall":"0.714","Precision@0.5":"0.702","Precision@0.6":"0.663","Precision@0.7":"0.585","Precision@0.8":"0.428","Precision@0.9":"0.151"},"uses_additional_data":false},{"leaderboard":"/sota/referring-expression-segmentation-on-j-hmdb","task":"Referring Expression Segmentation","dataset":"J-HMDB","model":"VLIDE","rank_in_archive_order":3,"of":21,"metrics":{"AP":"0.441","IoU mean":"0.666","IoU overall":"0.68","Precision@0.5":"0.874","Precision@0.6":"0.791","Precision@0.7":"0.586","Precision@0.8":"0.182","Precision@0.9":"0.30"},"uses_additional_data":false},{"leaderboard":"/sota/referring-expression-segmentation-on-refer-1","task":"Referring Expression Segmentation","dataset":"Refer-YouTube-VOS (2021 public validation)","model":"VLIDE","rank_in_archive_order":31,"of":33,"metrics":{"F":"50.67","J":"48.44","J&F":"49.56"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.15969","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}