Effective deep learning-based multi-modal retrieval
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Summary
This paper proposes a general learning objective that effectively captures both intramodal and intermodal semantic relationships of data from heterogeneous sources and proposes two learning algorithms to realize it: an unsupervised approach that uses stacked auto-encoders and requires minimum prior knowledge on the training data and a supervised approach using deep convolutional neural network and neural language model.
- Type
- article
- Published
- 2015-07-19
- Cited by
- 122
- References
- 53
- OpenAlex
- https://openalex.org/W1202352811
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16741401
Keywords
Computer science, Artificial intelligence, Exploit, Deep learning, Machine learning
References
- Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
- Multimodal learning with deep Boltzmann machines
- Deep Convolutional Ranking for Multilabel Image Annotation
- A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces
- Efficient Estimation of Word Representations in Vector Space
- Saturating Auto-Encoders
- Accelerating t-SNE using tree-based algorithms
- Data fusion through cross-modality metric learning using similarity-sensitive hashing
- Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
- NUS-WIDE: a real-world web image database from National University of Singapore
- Extracting and composing robust features with denoising autoencoders
- A low rank structural large margin method for cross-modal ranking
- Inter-media hashing for large-scale retrieval from heterogeneous data sources
- A probabilistic model for multimodal hash function learning
- Index-driven similarity search in metric spaces (Survey Article)
- Multiple feature hashing for real-time large scale near-duplicate video retrieval
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- A new approach to cross-modal multimedia retrieval
- HashFile: An efficient index structure for multimedia data
- Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
Cited by
- SINGA: Putting Deep Learning in the Hands of Multimedia Users
- SINGA: A Distributed Deep Learning Platform
- Deep Learning at Scale and at Ease
- Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval
- Fine-grained representation learning in convolutional autoencoders
- Cross-Modal Retrieval via Deep and Bidirectional Representation Learning
- Binary code learning via optimal class representations
- A Comprehensive Survey on Cross-modal Retrieval
- Simultaneous Semi-Coupled Dictionary Learning for Matching RGBD Data
- Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems
- Multimodal Deep Embedding via Hierarchical Grounded Compositional Semantics
- Database Meets Deep Learning: Challenges and Opportunities
- Social network search based on semantic analysis and learning
- Deep Neural Network Boosted Large Scale Image Recognition Using User Click Data
- Heterogeneous Sensor Data Fusion By Deep Multimodal Encoding
- Deep model-based feature extraction for predicting protein subcellular localizations from bio-images
- Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier
- Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical Space
- A deep learning approach for web service interactions
- Multimedia information retrieval using fuzzy cluster-based model learning
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