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Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Mingdong Zhu, Derong Shen, Lixin Xu, Xianfang Wang
2021 Data Science and Engineering  
Cross-modal Query with Interpretability (MCQI) framework.  ...  The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability  ...  Cross-modal Hashing Deep cross-modal hashing (DCMH) [13] combines hashing learning and deep feature learning by preserving the semantic similarity between modalities.  ... 
doi:10.1007/s41019-021-00162-4 fatcat:7tdgbtoq2jc45ixrdltrl4nofu

Deep Robust Multilevel Semantic Cross-Modal Hashing [article]

Ge Song, Jun Zhao, Xiaoyang Tan
2020 arXiv   pre-print
It seeks to preserve fine-grained similarity among data with rich semantics, while explicitly require distances between dissimilar points to be larger than a specific value for strong robustness.  ...  Hashing based cross-modal retrieval has recently made significant progress.  ...  They [11; 13] focus on preserving simple similarity structures (i.e., similar or dissimilar) rather than more fine-grained ones, and the used similarity information is often very sparse.  ... 
arXiv:2002.02698v2 fatcat:cemti3qovrgdrki2i4u3epcry4

Deep Multi-Semantic Fusion-Based Cross-Modal Hashing

Xinghui Zhu, Liewu Cai, Zhuoyang Zou, Lei Zhu
2022 Mathematics  
However, the existing deep hashing methods cannot consider multi-label semantic learning and cross-modal similarity learning simultaneously.  ...  That means potential semantic correlations among multimedia data are not fully excavated from multi-category labels, which also affects the original similarity preserving of cross-modal hash codes.  ...  Cross-modal hamming hashing (CMHH) [59] learns high-quality hash representations to significantly penalize similar cross-modal pairs with Hamming distances larger than the Hamming radius threshold.  ... 
doi:10.3390/math10030430 fatcat:yri6dbd53zglhc77wtpswxgsoa

Tag-based Weakly-supervised Hashing for Image Retrieval

Ziyu Guan, Fei Xie, Wanqing Zhao, Xiaopeng Wang, Long Chen, Wei Zhao, Jinye Peng
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
fine-tuning.  ...  We are concerned with using user-tagged images to learn proper hashing functions for image retrieval.  ...  Tagging data offers the opportunity to train hashing models to capture fine-grained similarity relationships between images. However, challenges always come with opportunities.  ... 
doi:10.24963/ijcai.2018/525 dblp:conf/ijcai/GuanXZW0ZP18 fatcat:lmwgosksafg7xgkvzcjgkrkj34

Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval [article]

Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song
2020 arXiv   pre-print
In particular, features from a sketch and a photo are enriched using cross-modal co-attention, coupled with hierarchical node fusion at every level to form a better embedding space to conduct retrieval  ...  Sketch as an image search query is an ideal alternative to text in capturing the fine-grained visual details.  ...  Fine-grained SBIR: Unlike category-level SBIR, fine-grained SBIR aims at instance-level matching.  ... 
arXiv:2007.15103v2 fatcat:owqu4cb6ujgvzbaj4wxjbm7l2i

A Review of Hashing Methods for Multimodal Retrieval

Wenming Cao, Wenshuo Feng, Qiubin Lin, Guitao Cao, Zhihai He
2020 IEEE Access  
With the advent of the information age, the amount of multimedia data has exploded. That makes fast and efficient retrieval in multimodal data become an urgent requirement.  ...  label information, especially the latest deep hashing methods.  ...  The principle of locality-sensitive hashing is to map samples with high similarity in the original space to the same hash bucket with higher probability, which ensures that the hash codes of the neighbor  ... 
doi:10.1109/access.2020.2968154 fatcat:e3vmte5hrnhu3b3lf5ws4gwnhm

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., +, TMM Jan. 2020 174-187 Online Fast Adaptive Low-Rank Similarity Learning for Cross-Modal Retrieval.  ...  Li, R., +, TMM Dec. 2020 3075-3087 Multi-Level Correlation Adversarial Hashing for Cross-Modal Retrieval.  ...  Image watermarking Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness. 2780 -2791 Low-Light Image Enhancement With Semi-Decoupled Decomposition.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Wang, X., +, TIP 2020 3039-3051 Bi-Modal Progressive Mask Attention for Fine-Grained Recognition. Complexity of Shapes Embedded in Zn With a Bias Towards Squares.  ...  Liu, J., +, TIP 2020 5244-5258 Deep Saliency Hashing for Fine-Grained Retrieval.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

Xiangyu Guo, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing  ...  between different media data and integrate feature learning with adversarial learning and hashing to build a unified semantic space for different media data.  ...  semantic similarity and is suitable for sparse and high-dimensional data scenarios. e literature [11] extended DMM using word embeddings as external extended knowledge to propose the GPU-DMM model,  ... 
doi:10.1155/2022/5283439 pmid:35586100 pmcid:PMC9110143 fatcat:3spnwxissndh3jpuvdu7uqq45a

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
., +, TMM 2021 3907-3918 Mask Cross-Modal Hashing Networks. Lin, Q., +, TMM 2021 550-558 Online Hashing With Bit Selection for Image Retrieval.  ...  ., +, TMM 2021 2413-2427 Fine-Grained Visual Categorization by Localizing Object Parts With Single Image.  ...  ., Low-Rank Pairwise Align- ment Bilinear Network For Few-Shot Fine-Grained Image Classification; TMM 2021 1666-1680 Huang, H., see 1855 -1867 Huang, H., see Jiang, X., TMM 2021 2602-2613 Huang, J.,  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

A Deep Semantic Alignment Network for Cross-Modal Image-Text Retrieval in Remote Sensing

Qimin Cheng, Yuzhuo Zhou, Peng Fu, Yuan Xu, Liang Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The purpose of cross-modal retrieval is to obtain the result data in one modality (e.g., image) which is semantically similar to the query data in another modality (e.g., text).  ...  Because of the rapid growth of multi-modal data from internet and social media, cross-modal retrieval has become an important and valuable task in recent years.  ...  [8] , their study did not directly map the entire image and sentence to the cross-modal embedding space, but rather mapped the more fine-grained image features and text features to a latent cross-modal  ... 
doi:10.1109/jstars.2021.3070872 fatcat:twe5xkahfva35esesaj5y6sd5i

Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Qibin Zheng, Xiaoguang Ren, Yi Liu, Wei Qin
2020 Mathematical Problems in Engineering  
Finally, the cross-modal similarity can be measured with the consistency between the semantic structures.  ...  The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled  ...  [18] proposed a sparse multimodal hashing method for cross-modal retrieval. Song et al.  ... 
doi:10.1155/2020/2503137 fatcat:37cxcuimjbfa7mdibjmeduztwe

Zero-Shot Sketch-Image Hashing [article]

Yuming Shen, Li Liu, Fumin Shen, Ling Shao
2018 arXiv   pre-print
To the best of our knowledge, ZSIH is the first zero-shot hashing work suitable for SBIR and cross-modal search.  ...  Providing training and test data subjected to a fixed set of pre-defined categories, the cutting-edge SBIR and cross-modal hashing works obtain acceptable retrieval performance.  ...  As an extension of conventional data hashing techniques [20, 51, 21, 53] , cross-modal hashing [4, 13, 71, 37, 34, 27, 5, 6] show great potential in retrieving heterogeneous data with high efficiency  ... 
arXiv:1803.02284v1 fatcat:yqpkfbps7feiplp3l2tp2roez4

Sequential Learning for Cross-Modal Retrieval

Ge Song, Xiaoyang Tan
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
Cross-modal retrieval has attracted increasing attention with the rapid growth of multimodal data, but its learning paradigm under changing environment is less studied.  ...  Extensive experiments are conducted on three popular multimodal datasets, showing that our method achieves state-of-the-art cross-modal retrieval performance without any modal-alignment. * Corresponding  ...  The hashing method [19, 31, 16, 26] seeks to encode high-dimensional features into compact binary codes, hence enabling fast similarity search with Hamming distances. Li et al.  ... 
doi:10.1109/iccvw.2019.00554 dblp:conf/iccvw/SongT19 fatcat:wq7czx7byvhsngmhhwzofsvde4

Table of contents

2020 IEEE Transactions on Image Processing  
Tourneret 5324 Deep Saliency Hashing for Fine-Grained Retrieval ........... S. Jin, H. Yao, X. Sun, S. Zhou, L. Zhang, and X.  ...  Netto 1329 Collective Affinity Learning for Partial Cross-Modal Hashing ......................................... J. Guo and W.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku
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