Proceedings of the 2022 International Conference on Multimedia Retrieval最新文献_第2页

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval 跨模态检索的多语义结构保持量化

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531417

Lei Zhu, Liewu Cai, Jiayu Song, Xinghui Zhu, Chengyuan Zhang, Shichao Zhang

引用次数: 3

DiGAN: Directional Generative Adversarial Network for Object Transfiguration 面向对象变形的定向生成对抗网络

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531400

Zhen Luo, Yingfang Zhang, Pei Zhong, Jingjing Chen, Donglong Chen

引用次数: 0

VideoCLIP: A Cross-Attention Model for Fast Video-Text Retrieval Task with Image CLIP 基于图像剪辑的快速视频文本检索任务的交叉注意模型

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531429

Yikang Li, Jenhao Hsiao, C. Ho

引用次数: 3

Selective Hypergraph Convolutional Networks for Skeleton-based Action Recognition 基于骨架的动作识别的选择性超图卷积网络

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531367

Yiran Zhu, Guangji Huang, Xing Xu, Yanli Ji, Fumin Shen

{"title":"Selective Hypergraph Convolutional Networks for Skeleton-based Action Recognition","authors":"Yiran Zhu, Guangji Huang, Xing Xu, Yanli Ji, Fumin Shen","doi":"10.1145/3512527.3531367","DOIUrl":"https://doi.org/10.1145/3512527.3531367","url":null,"abstract":"In skeleton-based action recognition, Graph Convolutional Networks (GCNs) have achieved remarkable performance since the skeleton representation of human action can be naturally modeled by the graph structure. Most of the existing GCN-based methods extract skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale contextual information. Besides, the commonly used strided convolution in temporal dimension could evenly filters out the keyframes we expect to preserve and leads to the loss of keyframe information. To address these issues, we propose a novel Selective Hypergraph Convolution Network, dubbed Selective-HCN, which stacks two key modules: Selective-scale Hypergraph Convolution (SHC) and Selective-frame Temporal Convolution (STC). The SHC module represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales. Instead of traditional strided temporal convolution, the STC module can adaptively select keyframes and filter redundant frames according to the importance of the frames. Extensive experiments on two challenging skeleton action benchmarks, i.e., NTU-RGB+D and Skeleton-Kinetics, demonstrate the superiority and effectiveness of our proposed method.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121872394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Flexible Order Aware Sequential Recommendation 灵活的顺序感知顺序推荐

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531407

Mingda Qian, Xiaoyan Gu, Lingyang Chu, Feifei Dai, Haihui Fan, Borang Li

{"title":"Flexible Order Aware Sequential Recommendation","authors":"Mingda Qian, Xiaoyan Gu, Lingyang Chu, Feifei Dai, Haihui Fan, Borang Li","doi":"10.1145/3512527.3531407","DOIUrl":"https://doi.org/10.1145/3512527.3531407","url":null,"abstract":"Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124288621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

EmoMTB: Emotion-aware Music Tower Blocks EmoMTB:情感感知音乐塔楼

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531351

Alessandro B. Melchiorre, D. Penz, Christian Ganhör, Oleg Lesota, Vasco Fragoso, Florian Friztl, Emilia Parada-Cabaleiro, Franz Schubert, M. Schedl

引用次数: 1

Fashion Style-Aware Embeddings for Clothing Image Retrieval 服装图像检索的时尚风格感知嵌入

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531433

Rino Naka, Marie Katsurai, Keisuke Yanagi, Ryosuke Goto

引用次数: 4

Weakly Supervised Pediatric Bone Age Assessment Using Ultrasonic Images via Automatic Anatomical RoI Detection 基于自动解剖RoI检测的超声图像弱监督儿童骨龄评估

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531436

Yunyan Yan, Chuanbin Liu, Hongtao Xie, Sicheng Zhang, Zhendong Mao

{"title":"Weakly Supervised Pediatric Bone Age Assessment Using Ultrasonic Images via Automatic Anatomical RoI Detection","authors":"Yunyan Yan, Chuanbin Liu, Hongtao Xie, Sicheng Zhang, Zhendong Mao","doi":"10.1145/3512527.3531436","DOIUrl":"https://doi.org/10.1145/3512527.3531436","url":null,"abstract":"Bone age assessment (BAA) is vital in pediatric clinical diagnosis. Existing deep learning methods predict bone age based on Regions of Interest (RoIs) detection or segmentation of hand radiograph, which requires expensive annotations. Limitations of radiographic technique on imaging and cost hinder their clinical application as well. Compared to X-ray images, ultrasonic images are rather clean, cheap and flexible, but the deep learning research on ultrasonic BAA is still a white space. For this purpose, we propose a weakly supervised interpretable framework entitled USB-Net, utilizing ultrasonic pelvis images and only image-level age annotations. USB-Net consists of automatic anatomical RoI detection stage and age assessment stage. In the detection stage, USB-Net locates the discriminative anatomical RoIs of pelvis through attention heatmap without any extra RoI supervision. In the assessment stage, the cropped anatomical RoI patch is fed as fine-grained input to estimate age. In addition, we provide the first ultrasonic BAA dataset composed of 1644 ultrasonic hip joint images with image-level labels of age and gender. The experimental results verify that our model keeps consistent attention with human knowledge and achieves 16.24 days mean absolute error (MAE) on USBAA dataset.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124525910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Music-to-Dance Generation with Multiple Conformer 具有多重一致性的音乐-舞蹈世代

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531430

Mingao Zhang, Changhong Liu, Yong Chen, Zhenchun Lei, Mingwen Wang

{"title":"Music-to-Dance Generation with Multiple Conformer","authors":"Mingao Zhang, Changhong Liu, Yong Chen, Zhenchun Lei, Mingwen Wang","doi":"10.1145/3512527.3531430","DOIUrl":"https://doi.org/10.1145/3512527.3531430","url":null,"abstract":"It is necessary for the music-to-dance generation to consider both the kinematics in dance that is highly complex and non-linear and the connection between music and dance movement that is far from deterministic. Existing approaches attempt to address the limited creativity problem, but it is still a very challenging task. First, it is a long-term sequence-to-sequence task. Second, it is noisy in the extracted motion keypoints. Last, there exist local and global dependencies in the music sequence and the dance motion sequence. To address these issues, we propose a novel autoregressive generative framework that predicts future motions based on past motions and music. This framework contains a music conformer, a motion conformer, and a cross-modal conformer, which utilizes the conformer to encode music and motion sequences, and further adapt the cross-modal conformer to the noisy dance motion data that enable it to not only capture local and global dependencies among the sequences but also reduce the effect of noisy data. Quantitative and qualitative experimental results on the publicly available music-to-dance dataset demonstrate our method improves greatly upon the baselines and can generate long-term coherent dance motions well-coordinated with the music.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"s1-16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127192861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

CLIP4Hashing: Unsupervised Deep Hashing for Cross-Modal Video-Text Retrieval CLIP4Hashing:用于跨模态视频文本检索的无监督深度哈希

Proceedings of the 2022 International Conference on Multimedia Retrieval Pub Date : 2022-06-27 DOI: 10.1145/3512527.3531381

Yaoxin Zhuo, Yikang Li, Jenhao Hsiao, C. Ho, Baoxin Li

{"title":"CLIP4Hashing: Unsupervised Deep Hashing for Cross-Modal Video-Text Retrieval","authors":"Yaoxin Zhuo, Yikang Li, Jenhao Hsiao, C. Ho, Baoxin Li","doi":"10.1145/3512527.3531381","DOIUrl":"https://doi.org/10.1145/3512527.3531381","url":null,"abstract":"With the ever-increasing multimedia data on the Web, cross-modal video-text retrieval has received a lot of attention in recent years. Deep cross-modal hashing approaches utilize the Hamming space for achieving fast retrieval. However, most existing algorithms have difficulties in seeking or constructing a well-defined joint semantic space. In this paper, an unsupervised deep cross-modal video-text hashing approach (CLIP4Hashing) is proposed, which mitigates the difficulties in bridging between different modalities in the Hamming space through building a single hashing net by employing the pre-trained CLIP model. The approach is enhanced by two novel techniques, the dynamic weighting strategy and the design of the min-max hashing layer, which are found to be the main sources of the performance gain. Compared with conventional deep cross-modal hashing algorithms, CLIP4Hashing does not require data-specific hyper-parameters. With evaluation using three challenging video-text benchmark datasets, we demonstrate that CLIP4Hashing is able to significantly outperform existing state-of-the-art hashing algorithms. Additionally, with larger bit sizes (e.g., 2048 bits), CLIP4Hashing can even deliver competitive performance compared with the results based on non-hashing features.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132889463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9