ACM Multimedia Asia最新文献_第3页

CFCR: A Convolution and Fusion Model for Cross-platform Recommendation 跨平台推荐的卷积和融合模型

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3495639

Shengze Yu, Xin Wang, Wenwu Zhu

{"title":"CFCR: A Convolution and Fusion Model for Cross-platform Recommendation","authors":"Shengze Yu, Xin Wang, Wenwu Zhu","doi":"10.1145/3469877.3495639","DOIUrl":"https://doi.org/10.1145/3469877.3495639","url":null,"abstract":"With the emergence of various online platforms, associating different platforms is playing an increasingly important role in many applications. Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing methods do not fully exploit high-order nonlinear connectivity information in cross-domain recommendation scenario and suffer from domain-incompatibility problem. In this paper, we propose an end-to-end convolution and fusion model for cross-platform recommendation (CFCR). The proposed CFCR model utilizes Graph Convolution Networks (GCN) to extract user and item features on graphs from different platforms, and fuses cross-platform information by Multimodal AutoEncoder (MAE) with common latent user features. Therefore, the high-order connectivity information is preserved to the most extent and domain-invariant user representations are automatically obtained. The domain-incompatible information is spontaneously discarded to avoid messing up the cross-platform association. Extensive experiments for the proposed CFCR model on real-world dataset demonstrate its advantages over existing cross-platform recommendation methods in terms of various evaluation metrics.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125183782","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

A Coarse-to-fine Approach for Fast Super-Resolution with Flexible Magnification 一种灵活放大的快速超分辨率从粗到精的方法

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490564

Zhichao Fu, Tianlong Ma, Liang Xue, Yingbin Zheng, Hao Ye, Liang He

引用次数: 0

Video Saliency Prediction via Deep Eye Movement Learning 基于深眼动学习的视频显著性预测

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490597

Jiazhong Chen, Jing Chen, Yuan Dong, Dakai Ren, Shiqi Zhang, Zongyi Li

引用次数: 2

Hierarchical Graph Representation Learning with Local Capsule Pooling 基于局部胶囊池的分层图表示学习

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3495645

Zidong Su, Zehui Hu, Yangding Li

引用次数: 5

A comparison study: the impact of age and gender distribution on age estimation 比较研究:年龄和性别分布对年龄估计的影响

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490576

Chang Kong, Qiuming Luo, Guoliang Chen

引用次数: 1

Hierarchical Composition Learning for Composed Query Image Retrieval 面向组合查询图像检索的分层组合学习

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490601

Yahui Xu, Yi Bin, Guoqing Wang, Yang Yang

{"title":"Hierarchical Composition Learning for Composed Query Image Retrieval","authors":"Yahui Xu, Yi Bin, Guoqing Wang, Yang Yang","doi":"10.1145/3469877.3490601","DOIUrl":"https://doi.org/10.1145/3469877.3490601","url":null,"abstract":"Composed query image retrieval is a growing research topic. The object is to retrieve images not only generally resemble the reference image, but differ according to the desired modification text. Existing methods mainly explore composing modification text with global feature or local entity descriptor of reference image. However, they ignore the fact that modification text is indeed diverse and arbitrary. It not only relates to abstractive global feature or concrete local entity transformation, but also often associates with the fine-grained structured visual adjustment. Thus, it is insufficient to emphasize the global or local entity visual for the query composition. In this work, we tackle this task by hierarchical composition learning. Specifically, the proposed method first encodes images into three representations consisting of global, entity and structure level representations. Structure level representation is richly explicable, which explicitly describes entities as well as attributes and relationships in the image with a directed graph. Based on these, we naturally perform hierarchical composition learning by fusing modification text and reference image in the global-entity-structure manner. It can transform the visual feature conditioned on modification text to target image in a coarse-to-fine manner, which takes advantage of the complementary information among three levels. Moreover, we introduce a hybrid space matching to explore global, entity and structure alignments which can get high performance and good interpretability.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134281777","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

Joint label refinement and contrastive learning with hybrid memory for Unsupervised Marine Object Re-Identification 联合标签细化和混合记忆对比学习用于无监督海洋物体再识别

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497695

Xiaorui Han, Zhiqi Chen, Ruixue Wang, Pengfei Zhao

{"title":"Joint label refinement and contrastive learning with hybrid memory for Unsupervised Marine Object Re-Identification","authors":"Xiaorui Han, Zhiqi Chen, Ruixue Wang, Pengfei Zhao","doi":"10.1145/3469877.3497695","DOIUrl":"https://doi.org/10.1145/3469877.3497695","url":null,"abstract":"Unsupervised object re-identification is a challenging task due to the missing of labels for the dataset. Many unsupervised object re-identification approaches combine clustering-based pseudo-label prediction with feature fine-tuning. These methods have achieved great success in the field of unsupervised object Re-ID. However, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinder the model’s capability on further improving feature representations. To this end, we propose a novel joint label refinement and contrastive learning framework with hybrid memory to alleviate this problem. Firstly, in order to reduce the noise of clustering pseudo labels, we propose a novel noise refinement strategy. This strategy refines pseudo labels at clustering phase and promotes clustering quality by boosting the label purity. In addition, we propose a hybrid memory bank. The hybrid memory dynamically generates prototype-level and un-clustered instance-level supervisory signals for learning feature representations. With all prototype-level and un-clustered instance-level supervisions, re-identification model is trained progressively. Our proposed unsupervised object Re-ID framework significantly reduces the influence of noisy labels and refines the learned features. Our method consistently achieves state-of-the-art performance on benchmark datasets.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131340551","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

Efficient Proposal Generation with U-shaped Network for Temporal Sentence Grounding 基于u型网络的时间句基础高效建议生成

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490606

Ludan Ruan, Qin Jin

引用次数: 0

Visible-Infrared Cross-Modal Person Re-identification based on Positive Feedback 基于正反馈的可见-红外跨模态人再识别

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497693

Lingyi Lu, Xin Xu

引用次数: 2

BRUSH: Label Reconstructing and Similarity Preserving Hashing for Cross-modal Retrieval 跨模态检索的标签重构和相似性保持哈希

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490589

P. Zhang, Pengfei Zhao, Xin Luo, Xin-Shun Xu

{"title":"BRUSH: Label Reconstructing and Similarity Preserving Hashing for Cross-modal Retrieval","authors":"P. Zhang, Pengfei Zhao, Xin Luo, Xin-Shun Xu","doi":"10.1145/3469877.3490589","DOIUrl":"https://doi.org/10.1145/3469877.3490589","url":null,"abstract":"The hashing technique has recently sparked much attention in information retrieval community due to its high efficiency in terms of storage and query processing. For cross-modal retrieval tasks, existing supervised hashing models either treat the semantic labels as the ground truth and formalize the problem to a classification task, or further add a similarity matrix as supervisory signals to pursue hash codes of high quality to represent coupled data. However, these approaches are incapable of ensuring that the learnt binary codes preserve well the semantics and similarity relationships contained in the supervised information. Moreover, for sophisticated discrete optimization problems, it is always addressed by continuous relaxation or bit-wise solver, which leads to a large quantization error and inefficient computation. To relieve these issues, in this paper, we present a two-step supervised discrete hashing method, i.e., laBel ReconstrUcting and Similarity preserving Hashing (BRUSH). We formulate it as an asymmetric pairwise similarity-preserving problem by using two latent semantic embeddings deducted from decomposing semantics and reconstructing semantics, respectively. Meanwhile, the unified binary codes are jointly generated based on both embeddings with the affinity guarantee, such that the discriminative property of the obtained hash codes can be significantly enhanced alongside preserving semantics well. In addition, by adopting two-step hash learning strategy, our method simplifies the procedure of the hashing function and binary codes learning, thus improving the flexibility and efficiency. The resulting discrete optimization problem is also elegantly solved by the proposed alternating algorithm without any relaxation. Extensive experiments on benchmarks demonstrate that BRUSH outperforms the state-of-the-art methods, in terms of efficiency and effectiveness.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121150961","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