Pub Date : 2024-08-14DOI: 10.1109/tmm.2024.3443634
Mao Cui, Yun Zhang, Chunling Fan, Raouf Hamzaoui, Qinglan Li
{"title":"Colored Point Cloud Quality Assessment Using Complementary Features in 3D and 2D Spaces","authors":"Mao Cui, Yun Zhang, Chunling Fan, Raouf Hamzaoui, Qinglan Li","doi":"10.1109/tmm.2024.3443634","DOIUrl":"https://doi.org/10.1109/tmm.2024.3443634","url":null,"abstract":"","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1109/TMM.2024.3384680
Wengang Zhou;Jiajun Deng;Niculae Sebe;Qi Tian;Alan L. Yuille;Concetto Spampinato;Zakia Hammal
In the ever-evolving domain of multimedia, the significance of multi-modality understanding cannot be overstated. As multimedia content becomes increasingly sophisticated and ubiquitous, the ability to effectively combine and analyze the diverse information from different types of data, such as text, audio, image, video and point clouds, will be paramount in pushing the boundaries of what technology can achieve in understanding and interacting with the world around us. Accordingly, multi-modality understanding has attracted a tremendous amount of research, establishing itself as an emerging topic. Pre-trained models, in particular, have revolutionized this field, providing a way to leverage vast amounts of data without task-specific annotation to facilitate various downstream tasks.
{"title":"Guest Editorial Introduction to the Issue on Pre-Trained Models for Multi-Modality Understanding","authors":"Wengang Zhou;Jiajun Deng;Niculae Sebe;Qi Tian;Alan L. Yuille;Concetto Spampinato;Zakia Hammal","doi":"10.1109/TMM.2024.3384680","DOIUrl":"10.1109/TMM.2024.3384680","url":null,"abstract":"In the ever-evolving domain of multimedia, the significance of multi-modality understanding cannot be overstated. As multimedia content becomes increasingly sophisticated and ubiquitous, the ability to effectively combine and analyze the diverse information from different types of data, such as text, audio, image, video and point clouds, will be paramount in pushing the boundaries of what technology can achieve in understanding and interacting with the world around us. Accordingly, multi-modality understanding has attracted a tremendous amount of research, establishing itself as an emerging topic. Pre-trained models, in particular, have revolutionized this field, providing a way to leverage vast amounts of data without task-specific annotation to facilitate various downstream tasks.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10616245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/tmm.2024.3428311
Ran Ran, Jiwei Wei, Chaoning Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen
{"title":"Adaptive Multi-scale Degradation-Based Attack for Boosting the Adversarial Transferability","authors":"Ran Ran, Jiwei Wei, Chaoning Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen","doi":"10.1109/tmm.2024.3428311","DOIUrl":"https://doi.org/10.1109/tmm.2024.3428311","url":null,"abstract":"","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1109/TMM.2024.3396272
Xun Jiang;Xing Xu;Zailei Zhou;Yang Yang;Fumin Shen;Heng Tao Shen
Given an untrimmed video and a text query, Video Moment Retrieval (VMR) aims at retrieving a specific moment where the video content is semantically related to the text query. Conventional VMR methods rely on video-text paired data or specific temporal annotations for each target event. However, the subjectivity and time-consuming nature of the labeling process limit their practicality in multimedia applications. To address this issue, recently researchers proposed a Zero-Shot Learning setting for VMR (ZS-VMR) that trains VMR models without manual supervision signals, thereby reducing the data cost. In this paper, we tackle the challenging ZS-VMR problem with Angular Reconstructive Text embeddings (ART) , generalizing the image-text matching pre-trained model CLIP to the VMR task. Specifically, assuming that visual embeddings are close to their semantically related text embeddings in angular space, our ART method generates pseudo-text embeddings of video event proposals through the hypersphere of CLIP. Moreover, to address the temporal nature of videos, we also design local multimodal fusion learning to narrow the gaps between image-text matching and video-text matching. Our experimental results on two widely used VMR benchmarks, Charades-STA and ActivityNet-Captions, show that our method outperforms current state-of-the-art ZS-VMR methods. It also achieves competitive performance compared to recent weakly-supervised VMR methods.
{"title":"Zero-Shot Video Moment Retrieval With Angular Reconstructive Text Embeddings","authors":"Xun Jiang;Xing Xu;Zailei Zhou;Yang Yang;Fumin Shen;Heng Tao Shen","doi":"10.1109/TMM.2024.3396272","DOIUrl":"10.1109/TMM.2024.3396272","url":null,"abstract":"Given an untrimmed video and a text query, Video Moment Retrieval (VMR) aims at retrieving a specific moment where the video content is semantically related to the text query. Conventional VMR methods rely on video-text paired data or specific temporal annotations for each target event. However, the subjectivity and time-consuming nature of the labeling process limit their practicality in multimedia applications. To address this issue, recently researchers proposed a Zero-Shot Learning setting for VMR (ZS-VMR) that trains VMR models without manual supervision signals, thereby reducing the data cost. In this paper, we tackle the challenging ZS-VMR problem with \u0000<italic>Angular Reconstructive Text embeddings (ART)</i>\u0000, generalizing the image-text matching pre-trained model CLIP to the VMR task. Specifically, assuming that visual embeddings are close to their semantically related text embeddings in angular space, our ART method generates pseudo-text embeddings of video event proposals through the hypersphere of CLIP. Moreover, to address the temporal nature of videos, we also design local multimodal fusion learning to narrow the gaps between image-text matching and video-text matching. Our experimental results on two widely used VMR benchmarks, Charades-STA and ActivityNet-Captions, show that our method outperforms current state-of-the-art ZS-VMR methods. It also achieves competitive performance compared to recent weakly-supervised VMR methods.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1109/tmm.2024.3428349
Wenfeng Song, Xuan Wang, Yuting Guo, Shuai Li, Bin Xia, Aimin Hao
{"title":"CenterFormer: A Novel Cluster Center Enhanced Transformer for Unconstrained Dental Plaque Segmentation","authors":"Wenfeng Song, Xuan Wang, Yuting Guo, Shuai Li, Bin Xia, Aimin Hao","doi":"10.1109/tmm.2024.3428349","DOIUrl":"https://doi.org/10.1109/tmm.2024.3428349","url":null,"abstract":"","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-15DOI: 10.1109/tmm.2024.3428317
Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, Yong Xu, Min Zhang
{"title":"LMEye: An Interactive Perception Network for Large Language Models","authors":"Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, Yong Xu, Min Zhang","doi":"10.1109/tmm.2024.3428317","DOIUrl":"https://doi.org/10.1109/tmm.2024.3428317","url":null,"abstract":"","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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