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Scalable Multi-grained Cross-modal Similarity Query with Interpretability
2021
Data Science and Engineering
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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]
2020
arXiv
pre-print
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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
2022
Mathematics
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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
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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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]
2020
arXiv
pre-print
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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
2020
IEEE Access
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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
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., +, 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
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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
2022
Computational Intelligence and Neuroscience
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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
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., +, 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
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
2020
Mathematical Problems in Engineering
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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]
2018
arXiv
pre-print
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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
2019
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
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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
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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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