Proceedings of the 2nd ACM International Conference on Multimedia in Asia最新文献_第6页

Fixed-size video summarization over streaming data via non-monotone submodular maximization 通过非单调次模最大化实现流数据的固定大小视频摘要

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446285

Ganfeng Lu, Jiping Zheng

{"title":"Fixed-size video summarization over streaming data via non-monotone submodular maximization","authors":"Ganfeng Lu, Jiping Zheng","doi":"10.1145/3444685.3446285","DOIUrl":"https://doi.org/10.1145/3444685.3446285","url":null,"abstract":"Video summarization which potentially fast browses a large amount of emerging video data as well as saves storage cost has attracted tremendous attentions in machine learning and information retrieval. Among existing efforts, determinantal point processes (DPPs) designed for selecting a subset of video frames to represent the whole video have shown great success in video summarization. However, existing methods have shown poor performance to generate fixed-size output summaries for video data, especially when video frames arrive in streaming manner. In this paper, we provide an efficient approach k-seqLS which summarizes streaming video data with a fixed-size k in vein of DPPs. Our k-seqLS approach can fully exploit the sequential nature of video frames by setting a time window and the frames outside the window have no influence on current video frame. Since the log-style of the DPP probability for each subset of frames is a non-monotone submodular function, local search as well as greedy techniques with cardinality constraints are adopted to make k-seqLS fixed-sized, efficient and with theoretical guarantee. Our experiments show that our proposed k-seqLS exhibits higher performance while maintaining practical running time.","PeriodicalId":119278,"journal":{"name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121059537","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: 1

Full-resolution encoder-decoder networks with multi-scale feature fusion for human pose estimation 基于多尺度特征融合的全分辨率编码器-解码器网络

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446282

Jie Ou, Mingjian Chen, Hong Wu

引用次数: 0

Scene graph generation via multi-relation classification and cross-modal attention coordinator 基于多关系分类和跨模态注意协调器的场景图生成

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446276

Xiaoyi Zhang, Zheng Wang, Xing Xu, Jiwei Wei, Yang Yang

{"title":"Scene graph generation via multi-relation classification and cross-modal attention coordinator","authors":"Xiaoyi Zhang, Zheng Wang, Xing Xu, Jiwei Wei, Yang Yang","doi":"10.1145/3444685.3446276","DOIUrl":"https://doi.org/10.1145/3444685.3446276","url":null,"abstract":"Scene graph generation intends to build graph-based representation from images, where nodes and edges respectively represent objects and relationships between them. However, scene graph generation today is heavily limited by imbalanced class prediction. Specifically, most of existing work achieves satisfying performance on simple and frequent relation classes (e.g. on), yet leaving poor performance with fine-grained and infrequent ones (e.g. walk on, stand on). To tackle this problem, in this paper, we redesign the framework as two branches, representation learning branch and classifier learning branch, for a more balanced scene graph generator. Furthermore, for representation learning branch, we propose Cross-modal Attention Coordinator (CAC) to gather consistent features from multi-modal using dynamic attention. For classifier learning branch, we first transfer relation classes' knowledge from large scale corpus, then we leverage Multi-Relationship classifier via Graph Attention neTworks (MR-GAT) to bridge the gap between frequent relations and infrequent ones. The comprehensive experimental results on VG200, a challenge dataset, indicate the competitiveness and the significant superiority of our proposed approach.","PeriodicalId":119278,"journal":{"name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114960445","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

Efficient inter-image relation graph neural network hashing for scalable image retrieval 高效的图像间关系图神经网络哈希可扩展图像检索

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446321

Hui Cui, Lei Zhu, Wentao Tan

{"title":"Efficient inter-image relation graph neural network hashing for scalable image retrieval","authors":"Hui Cui, Lei Zhu, Wentao Tan","doi":"10.1145/3444685.3446321","DOIUrl":"https://doi.org/10.1145/3444685.3446321","url":null,"abstract":"Unsupervised deep hashing is a promising technique for large-scale image retrieval, as it equips powerful deep neural networks and has advantage on label independence. However, the unsupervised deep hashing process needs to train a large amount of deep neural network parameters, which is hard to optimize when no labeled training samples are provided. How to maintain the well scalability of unsupervised hashing while exploiting the advantage of deep neural network is an interesting but challenging problem to investigate. With the motivation, in this paper, we propose a simple but effective Inter-image Relation Graph Neural Network Hashing (IRGNNH) method. Different from all existing complex models, we discover the latent inter-image semantic relations without any manual labels and exploit them further to assist the unsupervised deep hashing process. Specifically, we first parse the images to extract latent involved semantics. Then, relation graph convolutional network is constructed to model the inter-image semantic relations and visual similarity, which generates representation vectors for image relations and contents. Finally, adversarial learning is performed to seamlessly embed the constructed relations into the image hash learning process, and improve the discriminative capability of the hash codes. Experiments demonstrate that our method significantly outperforms the state-of-the-art unsupervised deep hashing methods on both retrieval accuracy and efficiency.","PeriodicalId":119278,"journal":{"name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129932555","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: 1

Cross-cultural design of facial expressions for humanoids: is there cultural difference between Japan and Denmark? 仿人面部表情的跨文化设计:日本和丹麦有文化差异吗?

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446294

I. Kanaya, Meina Tawaki, Keiko Yamamoto

引用次数: 0

Real-time arbitrary video style transfer 实时任意视频风格转换

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446301

Xingyu Liu, Zongxing Ji, Piao Huang, Tongwei Ren

引用次数: 0

Motion-transformer: self-supervised pre-training for skeleton-based action recognition 运动转换器:基于骨骼的动作识别的自监督预训练

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446289

Yi-Bin Cheng, Xipeng Chen, Dongyu Zhang, Liang Lin

引用次数: 21

Synthesized 3D models with smartphone based MR to modify the PreBuilt environment: interior design 综合3D模型与智能手机为基础的MR修改预建环境:室内设计

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446251

Anish Bhardwaj, N. Chauhan, R. Shah

引用次数: 0

Pulse localization networks with infrared camera 红外摄像机脉冲定位网络

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446318

Bohong Yang, Kai Meng, Hong Lu, Xinyao Nie, Guanhao Huang, Jingjing Luo, Xing Zhu

引用次数: 6

Self-supervised adversarial learning for cross-modal retrieval 跨模态检索的自监督对抗学习

Proceedings of the 2nd ACM International Conference on Multimedia in Asia Pub Date : 2021-03-07 DOI: 10.1145/3444685.3446269

Yangchao Wang, Shiyuan He, Xing Xu, Yang Yang, Jingjing Li, Heng Tao Shen

{"title":"Self-supervised adversarial learning for cross-modal retrieval","authors":"Yangchao Wang, Shiyuan He, Xing Xu, Yang Yang, Jingjing Li, Heng Tao Shen","doi":"10.1145/3444685.3446269","DOIUrl":"https://doi.org/10.1145/3444685.3446269","url":null,"abstract":"Cross-modal retrieval aims at enabling flexible retrieval across different modalities. The core of cross-modal retrieval is to learn projections for different modalities and make instances in the learned common subspace comparable to each other. Self-supervised learning automatically creates a supervision signal by transformation of input data and learns semantic features by training to predict the artificial labels. In this paper, we proposed a novel method named Self-Supervised Adversarial Learning (SSAL) for Cross-Modal Retrieval, which deploys self-supervised learning and adversarial learning to seek an effective common subspace. A feature projector tries to generate modality-invariant representations in the common subspace that can confuse an adversarial discriminator consists of two classifiers. One of the classifiers aims to predict rotation angle from image representations, while the other classifier tries to discriminate between different modalities from the learned embeddings. By confusing the self-supervised adversarial model, feature projector filters out the abundant high-level visual semantics and learns image embeddings that are better aligned with text modality in the common subspace. Through the joint exploitation of the above, an effective common subspace is learned, in which representations of different modlities are aligned better and common information of different modalities is well preserved. Comprehensive experimental results on three widely-used benchmark datasets show that the proposed method is superior in cross-modal retrieval and significantly outperforms the existing cross-modal retrieval methods.","PeriodicalId":119278,"journal":{"name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129551921","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: 3