{"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/wnet-audio-guided-video-object-segmentation","title":"Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Wenwen Pan","Haonan Shi","Zhou Zhao","Jieming Zhu","Xiuqiang He","Zhigeng Pan","Lianli Gao","Jun Yu","Fei Wu","Qi Tian"],"abstract":"    Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions. However, the existing referring video semantic segmentation works mainly focus on the guidance of text-based referring expressions, due to the lack of modeling the semantic representation of audio-video interaction contents. In this paper, we consider the problem of audio-guided video semantic segmentation from the viewpoint of end-to-end denoised encoder-decoder network learning. We propose the walvelet-based encoder network to learn the crossmodal representations of the video contents with audio-form queries. Specifically, we adopt a multi-head cross-modal attention to explore the potential relations of video and query contents. A 2-dimension discrete wavelet transform is employed to decompose the audio-video features. We quantify the thresholds of high frequency coefficients to filter the noise and outliers. Then, a self attention-free decoder network is developed to generate the target masks with frequency domain transforms. Moreover, we maximize mutual information between the encoded features and multi-modal features after cross-modal attention to enhance the audio guidance. In addition, we construct the first large-scale audio-guided video semantic segmentation dataset. The extensive experiments show the effectiveness of our method.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_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":"wnet-audio-guided-video-object-segmentation","repo_url":"https://github.com/asudahkzj/wnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}