PPIS-JOIN: A Novel Privacy-Preserving Image Similarity Join Method

Zhang, Chengyuan; Xie, Fangxin; Yu, Hao; Zhang, Jianfeng; Zhu, Lei; Li, Yangding

doi:10.1007/s11063-021-10537-3

Cited by 6 publications

(4 citation statements)

References 76 publications

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“…In the self-attention blocks, the image regions feature and words feature are finally updated as follows, R * = (Att r2r R) × r imp (18) W * = (Att w2w W) × w imp (19) The details of accumulated importance are described in Section 3.3.5.…”

Section: Self-attention With Conditioning Gatesmentioning

confidence: 99%

“…At present, multi-modal learning has bridged the gap between visual and language and has been widely concerned [1][2][3][4][5]. Remarkable progress has been made in many multi-modal learning tasks, e.g., image captioning [6][7][8][9], video captioning [10][11][12], cross-modal retrieval [13][14][15][16][17][18][19][20][21][22], and visual question answering(VQA) [7,[23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Path-Wise Attention Memory Network for Visual Question Answering

et al. 2022

Self Cite

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Visual question answering (VQA) is regarded as a multi-modal fine-grained feature fusion task, which requires the construction of multi-level and omnidirectional relations between nodes. One main solution is the composite attention model which is composed of co-attention (CA) and self-attention(SA). However, the existing composite models only consider the stack of single attention blocks, lack of path-wise historical memory, and overall adjustments. We propose a path attention memory network (PAM) to construct a more robust composite attention model. After each single-hop attention block (SA or CA), the importance of the cumulative nodes is used to calibrate the signal strength of nodes’ features. Four memoried single-hop attention matrices are used to obtain the path-wise co-attention matrix of path-wise attention (PA); therefore, the PA block is capable of synthesizing and strengthening the learning effect on the whole path. Moreover, we use guard gates of the target modal to check the source modal values in CA and conditioning gates of another modal to guide the query and key of the current modal in SA. The proposed PAM is beneficial to construct a robust multi-hop neighborhood relationship between visual and language and achieves excellent performance on both VQA2.0 and VQA-CP V2 datasets.

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Section: Self-attention With Conditioning Gatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path-Wise Attention Memory Network for Visual Question Answering

et al. 2022

Self Cite

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“…Despite the efficiency of feature learning, the shallow model cannot capture non-linear highlevel semantic features, which impact retrieval accuracy. Thanks for the rapid development of deep learning technology, lots of deep model based cross-modal retrieval approaches have been proposed [5,18,42]. Compared with traditional methods, these deep models have stronger capabilities to learn non-linear semantic correlation from multi-modal contents.…”

Section: Introductionmentioning

confidence: 99%

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

Zhu

Cai

Song

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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Cross-modal hashing is a hot issue in the multimedia community, which is to generate compact hash code from multimedia content for efficient cross-modal search. Two challenges, i.e., (1) How to efficiently enhance cross-modal semantic mining is essential for cross-modal hash code learning, and (2) How to combine multiple semantic correlations learning to improve the semantic similarity preserving, cannot be ignored. To this end, this paper proposed a novel end-to-end cross-modal hashing approach, named Multiple Semantic Structure-Preserving Quantization (MSSPQ) that is to integrate deep hashing model with multiple semantic correlation learning to boost hash learning performance. The multiple semantic correlation learning consists of inter-modal and intra-modal pairwise correlation learning and Cosine correlation learning, which can comprehensively capture cross-modal consistent semantics and realize semantic similarity preserving. Extensive experiments are conducted on three multimedia datasets, which confirms that the proposed method outperforms the baselines. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval.

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“…The GraphSAGE model is an inductive learning framework that can efficiently generate unknown vertices embedding by using the attribute information of vertices [6,7]. It is used to derive the user trust relationships from the original social network that hold both local and global information of the social network [8][9][10], and a graph-embedded model-based collaborative filtering recommendation algorithm is proposed. Intuitively, the low-dimensional feature representation of user nodes in social networks can be learned through graph embedding and can be integrated into traditional social network-based recommendation algorithms to address the problems of coarse granularity.…”

Section: Introductionmentioning

confidence: 99%

Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

Song

Yuan

et al. 2022

Future Internet

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With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.

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PPIS-JOIN: A Novel Privacy-Preserving Image Similarity Join Method

Cited by 6 publications

References 76 publications

Path-Wise Attention Memory Network for Visual Question Answering

Path-Wise Attention Memory Network for Visual Question Answering

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

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