IEEE Transactions on Multimedia最新文献

{"title":"CarveNet: Carving Point-Block for Complex 3D Shape Completion","authors":"Qing Guo, Zhijie Wang, Lubo Wang, Haotian Dong, Felix Juefei-Xu, Di Lin, Lei Ma, Wei Feng, Yang Liu","doi":"10.1109/tmm.2024.3443613","DOIUrl":"https://doi.org/10.1109/tmm.2024.3443613","url":null,"abstract":"","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"54 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178738","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}

{"title":"Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-Labeling","authors":"Xu Wang;Yifan Li;Qiudan Zhang;Wenhui Wu;Mark Junjie Li;Lin Ma;Jianmin Jiang","doi":"10.1109/TMM.2024.3443670","DOIUrl":"10.1109/TMM.2024.3443670","url":null,"abstract":"Learning to build 3D scene graphs is essential for real-world perception in a structured and rich fashion. However, previous 3D scene graph generation methods utilize a fully supervised learning manner and require a large amount of entity-level annotation data of objects and relations, which is extremely resource-consuming and tedious to obtain. To tackle this problem, we propose 3D-VLAP, a weakly-supervised 3D scene graph generation method via Visual-Linguistic Assisted Pseudo-labeling. Specifically, our 3D-VLAP exploits the superior ability of current large-scale visual-linguistic models to align the semantics between texts and 2D images, as well as the naturally existing correspondences between 2D images and 3D point clouds, and thus implicitly constructs correspondences between texts and 3D point clouds. First, we establish the positional correspondence from 3D point clouds to 2D images via camera intrinsic and extrinsic parameters, thereby achieving alignment of 3D point clouds and 2D images. Subsequently, a large-scale cross-modal visual-linguistic model is employed to indirectly align 3D instances with the textual category labels of objects by matching 2D images with object category labels. The pseudo labels for objects and relations are then produced for 3D-VLAP model training by calculating the similarity between visual embeddings and textual category embeddings of objects and relations encoded by the visual-linguistic model, respectively. Ultimately, we design an edge self-attention based graph neural network to generate scene graphs of 3D point clouds. Experiments demonstrate that our 3D-VLAP achieves comparable results with current fully supervised methods, meanwhile alleviating the data annotation pressure.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11164-11175"},"PeriodicalIF":8.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178737","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}

{"title":"Controllable Syllable-Level Lyrics Generation From Melody With Prior Attention","authors":"Zhe Zhang;Yi Yu;Atsuhiro Takasu","doi":"10.1109/TMM.2024.3443664","DOIUrl":"10.1109/TMM.2024.3443664","url":null,"abstract":"Melody-to-lyrics generation, which is based on syllable-level generation, is an intriguing and challenging topic in the interdisciplinary field of music, multimedia, and machine learning. Many previous research projects generate word-level lyrics sequences due to the lack of alignments between syllables and musical notes. Moreover, controllable lyrics generation from melody is also less explored but important for facilitating humans to generate diverse desired lyrics. In this work, we propose a controllable melody-to-lyrics model that is able to generate syllable-level lyrics with user-desired rhythm. An explicit n-gram (EXPLING) loss is proposed to train the Transformer-based model to capture the sequence dependency and alignment relationship between melody and lyrics and predict the lyrics sequences at the syllable level. A prior attention mechanism is proposed to enhance the controllability and diversity of lyrics generation. Experiments and evaluation metrics verified that our proposed model has the ability to generate higher-quality lyrics than previous methods and the feasibility of interacting with users for controllable and diverse lyrics generation. We believe this work provides valuable insights into human-centered AI research in music generation tasks. The source codes for this work will be made publicly available for further reference and exploration.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11083-11094"},"PeriodicalIF":8.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178740","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}

{"title":"Anti-Collapse Loss for Deep Metric Learning","authors":"Xiruo Jiang;Yazhou Yao;Xili Dai;Fumin Shen;Liqiang Nie;Heng-Tao Shen","doi":"10.1109/TMM.2024.3443616","DOIUrl":"10.1109/TMM.2024.3443616","url":null,"abstract":"Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize inter-class discrepancy and minimize intra-class diversity. However, these methods tend to suffer from the collapse of the embedding space due to their over-reliance on label information. This leads to sub-optimal feature representation and inferior model performance. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Specifically, our proposed loss primarily draws inspiration from the principle of Maximal Coding Rate Reduction. It promotes the sparseness of feature clusters in the embedding space to prevent collapse by maximizing the average coding rate of sample features or class proxies. Moreover, we integrate our proposed loss with pair-based and proxy-based methods, resulting in notable performance improvement. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of our method in preventing embedding space collapse and promoting generalization performance.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11139-11150"},"PeriodicalIF":8.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178739","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}

{"title":"Gist, Content, Target-Oriented: A 3-Level Human-Like Framework for Video Moment Retrieval","authors":"Di Wang;Xiantao Lu;Quan Wang;Yumin Tian;Bo Wan;Lihuo He","doi":"10.1109/TMM.2024.3443672","DOIUrl":"10.1109/TMM.2024.3443672","url":null,"abstract":"Video moment retrieval (VMR) aims to locate corresponding moments in an untrimmed video via a given natural language query. While most existing approaches treat this task as a cross-modal content matching or boundary prediction problem, recent studies have started to solve the VMR problem from a reading comprehension perspective. However, the cross-modal interaction processes of existing models are either insufficient or overly complex. Therefore, we reanalyze human behaviors in the document fragment location task of reading comprehension, and design a specific module for each behavior to propose a 3-level human-like moment retrieval framework (Tri-MRF). Specifically, we summarize human behaviors such as grasping the general structures of the document and the question separately, cross-scanning to mark the direct correspondences between keywords in the document and in the question, and summarizing to obtain the overall correspondences between document fragments and the question. Correspondingly, the proposed Tri-MRF model contains three modules: 1) a gist-oriented intra-modal comprehension module is used to establish contextual dependencies within each modality; 2) a content-oriented fine-grained comprehension module is used to explore direct correspondences between clips and words; and 3) a target-oriented integrated comprehension module is used to verify the overall correspondence between the candidate moments and the query. In addition, we introduce a biconnected GCN feature enhancement module to optimize query-guided moment representations. Extensive experiments conducted on three benchmarks, TACoS, ActivityNet Captions and Charades-STA demonstrate that the proposed framework outperforms State-of-the-Art methods.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11044-11056"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178711","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}

{"title":"Sparse Pedestrian Character Learning for Trajectory Prediction","authors":"Yonghao Dong;Le Wang;Sanping Zhou;Gang Hua;Changyin Sun","doi":"10.1109/TMM.2024.3443591","DOIUrl":"10.1109/TMM.2024.3443591","url":null,"abstract":"Pedestrian trajectory prediction in a first-person view has recently attracted much attention due to its importance in autonomous driving. Recent work utilizes pedestrian character information, i.e., action and appearance, to improve the learned trajectory embedding and achieves state-of-the-art performance. However, it neglects the invalid and negative pedestrian character information, which is harmful to trajectory representation and thus leads to performance degradation. To address this issue, we present a two-stream sparse-character-based network (TSNet) for pedestrian trajectory prediction. Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream. Moreover, to model the negative-removed characters, we propose a novel sparse character graph, including the sparse category and sparse temporal character graphs, to learn the different effects of various characters in category and temporal dimensions, respectively. Extensive experiments on two first-person view datasets, PIE and JAAD, show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate different effects of various characters and prove that TSNet outperforms approaches without eliminating negative characters.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11070-11082"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178744","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}

{"title":"HSSHG: Heuristic Semantics-Constrained Spatio-Temporal Heterogeneous Graph for VideoQA","authors":"Ruomei Wang;Yuanmao Luo;Fuwei Zhang;Mingyang Liu;Xiaonan Luo","doi":"10.1109/TMM.2024.3443661","DOIUrl":"10.1109/TMM.2024.3443661","url":null,"abstract":"Video question answering is a challenging task that requires models to recognize visual information in videos and perform spatio-temporal reasoning. Current models increasingly focus on enabling objects spatio-temporal reasoning via graph neural networks. However, the existing graph network-based models still have deficiencies when constructing the spatio-temporal relationship between objects: (1) The lack of consideration of the spatio-temporal constraints between objects when defining the adjacency relationship; (2) The semantic correlation between objects is not fully considered when generating edge weights. These make the model lack representation of spatio-temporal interaction between objects, which directly affects the ability of object relation reasoning. To solve the above problems, this paper designs a heuristic semantics-constrained spatio-temporal heterogeneous graph, employing a semantic consistency-aware strategy to construct the spatio-temporal interaction between objects. The spatio-temporal relationship between objects is constrained by the object co-occurrence relationship and the object consistency. The plot summaries and object locations are used as heuristic semantic priors to constrain the weights of spatial and temporal edges. The spatio-temporal heterogeneity graph more accurately restores the spatio-temporal relationship between objects and strengthens the model's object spatio-temporal reasoning ability. Based on the spatio-temporal heterogeneous graph, this paper proposes Heuristic Semantics-constrained Spatio-temporal Heterogeneous Graph for VideoQA (HSSHG), which achieves state-of-the-art performance on benchmark MSVD-QA and FrameQA datasets, and demonstrates competitive results on benchmark MSRVTT-QA and ActivityNet-QA dataset. Extensive ablation experiments verify the effectiveness of each component in the network and the rationality of hyperparameter settings, and qualitative analysis verifies the object-level spatio-temporal reasoning ability of HSSHG.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11176-11190"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178603","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}

{"title":"MMVS: Enabling Robust Adaptive Video Streaming for Wildly Fluctuating and Heterogeneous Networks","authors":"Shuoyao Wang;Jiawei Lin;Yu Dai","doi":"10.1109/TMM.2024.3443609","DOIUrl":"10.1109/TMM.2024.3443609","url":null,"abstract":"With the advancement of wireless technology, the fifth-generation mobile communication network (5G) has the capability to provide exceptionally high bandwidth for supporting high-quality video streaming services. Nevertheless, this network exhibits substantial fluctuations, posing a significant challenge in ensuring the reliability of video streaming services. This research introduces a novel algorithm, the Multi-type data perception-based Meta-learning-enabled adaptive Video Streaming algorithm (MMVS), designed to adapt to diverse network conditions, encompassing 3G and mmWave 5G networks. The proposed algorithm integrates the proximal policy optimization technique with the meta-learning framework to cope with the gradient estimation noise in network fluctuation. To further improve the robustness of the algorithm, MMVS introduces meta advantage normalization. Additionally, MMVS treats network information as multiple types of input data, thus enabling the precise definition of distinct network structures for perceiving them accurately. The experimental results on network trace datasets in real-world scenarios illustrate that MMVS is capable of delivering an additional 6% average QoE in mmWave 5G network, and outperform the representative benchmarks in six pairs of heterogeneous networks and user preferences.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11018-11030"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178720","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}

{"title":"GS-SFS: Joint Gaussian Splatting and Shape-From-Silhouette for Multiple Human Reconstruction in Large-Scale Sports Scenes","authors":"Yuqi Jiang;Jing Li;Haidong Qin;Yanran Dai;Jing Liu;Guodong Zhang;Canbin Zhang;Tao Yang","doi":"10.1109/TMM.2024.3443637","DOIUrl":"10.1109/TMM.2024.3443637","url":null,"abstract":"We introduce GS-SFS, a method that utilizes a camera array with wide baselines for high-quality multiple human mesh reconstruction in large-scale sports scenes. Traditional human reconstruction methods in sports scenes, such as Shape-from-Silhouette (SFS), struggle with sparse camera setups and small human targets, making it challenging to obtain complete and accurate human representations. Despite advances in differentiable rendering, including 3D Gaussian Splatting (3DGS), which can produce photorealistic novel-view renderings with dense inputs, accurate depiction of surfaces and generation of detailed meshes is still challenging. Our approach uniquely combines 3DGS's view synthesis with an optimized SFS method, thereby significantly enhancing the quality of multiperson mesh reconstruction in large-scale sports scenes. Specifically, we introduce body shape priors, including the human surface point clouds extracted through SFS and human silhouettes, to constrain 3DGS to a more accurate representation of the human body only. Then, we develop an improved mesh reconstruction method based on SFS, mainly by adding additional viewpoints through 3DGS and obtaining a more accurate surface to achieve higher-quality reconstruction models. We implement a high-density scene resampling strategy based on spherical sampling of human bounding boxes and render new perspectives using 3D Gaussian Splatting to create precise and dense multi-view human silhouettes. During mesh reconstruction, we integrate the human body's 2D Signed Distance Function (SDF) into the computation of the SFS's implicit surface field, resulting in smoother and more accurate surfaces. Moreover, we enhance mesh texture mapping by blending original and rendered images with different weights, preserving high-quality textures while compensating for missing details. The experimental results from real basketball game scenarios demonstrate the significant improvements of our approach for multiple human body model reconstruction in complex sports settings.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11095-11110"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178714","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}

{"title":"RCVS: A Unified Registration and Fusion Framework for Video Streams","authors":"Housheng Xie;Meng Sang;Yukuan Zhang;Yang Yang;Shan Zhao;Jianbo Zhong","doi":"10.1109/TMM.2024.3443673","DOIUrl":"10.1109/TMM.2024.3443673","url":null,"abstract":"The infrared and visible cross-modal registration and fusion can generate more comprehensive representations of object and scene information. Previous frameworks primarily focus on addressing the modality disparities and the impact of preserving diverse modality information on the performance of registration and fusion tasks among different static image pairs. However, these frameworks overlook the practical deployment on real-world devices, particularly in the context of video streams. Consequently, the resulting video streams often suffer from instability in registration and fusion, characterized by fusion artifacts and inter-frame jitter. In light of these considerations, this paper proposes a unified registration and fusion scheme for video streams, termed RCVS. It utilizes a robust matcher and spatial-temporal calibration module to achieve stable registration of video sequences. Subsequently, RCVS combines a fast lightweight fusion network to provide stable fusion video streams for infrared and visible imaging. Additionally, we collect a infrared and visible video dataset HDO, which comprises high-quality infrared and visible video data captured across diverse scenes. Our RCVS exhibits superior performance in video stream registration and fusion tasks, adapting well to real-world demands. Overall, our proposed framework and HDO dataset offer the first effective and comprehensive benchmark in this field, solving stability and real-time challenges in infrared and visible video stream fusion while assessing different solution performances to foster development in this area.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"11031-11043"},"PeriodicalIF":8.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178741","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}