[PDF] Top 20 Adversarial Learning Based Semantic Correlation Representation for Cross-Modal Retrieval
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Adversarial Learning Based Semantic Correlation Representation for Cross-Modal Retrieval
... With the rapid development of Internet and the widely usage of smart devices, huge amounts of multimedia data with various modalities, such as images, texts, videos and audios, etc. are generated, collected, stored and ... See full document
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Kernelizing Semantic Similarity Measurement Using Bi directional Learning Ranking for Cross Modal Retrieval
... inner semantic similarities between different modal data, cross-modal retrieval tries to map heterogenous features to a hidden common subspace in which they can be reasonably ... See full document
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Representation Learning Using Multi Task Deep Neural Networks for Semantic Classification and Information Retrieval
... learned based on ei- ther unsupervised objectives, which does not directly optimize the desired task, or single- task supervised objectives, which often suf- fer from insufficient training ...for learning ... See full document
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Cross language Learning with Adversarial Neural Networks
... on Semantic Textual Simi- larity (Agirre et ...a retrieval model for finding similar questions based on the similarity of syntactic trees, and Da San Martino et ...continuous semantic ... See full document
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Ranking-Based Deep Cross-Modal Hashing
... these semantic ranking methods just consider one modality, and cannot apply to cross-modal ...the cross- modal hashing ...are based on hand-crafted (or raw-level) ...feature ... See full document
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Exploring Multi-Modal and Structured Representation Learning for Visual Image and Video Understanding
... Dictionary learning [79] is a popular method for finding effective sparse representations of input ...scale cross-domain datasets, traditional DL approaches have been extended to cross-modal ... See full document
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Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
... modality representation and hash codes further can be learned based on the achieved ...modality correlation is learned by exploiting manifold structure between differ- ent ...modality ... See full document
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Cross modal Retrieval of Chinese CQA Based on CCA Algorithm
... Traditional machine learning K-means clustering method is used to extract image features. The feature extracted from the concatenated neural network full-connected layer is a comprehensive representation of ... See full document
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Semantic Correlation based Deep Cross Modal Hashing for Faster Retrieval
... deep based cross-modal hashing techniques are currently hot research topic where feature extraction as well as generation of the hash code can be achieved in the common ...Deep ... See full document
5
Self supervised Adversarial Hashing Cross modal Retrieval with Generative Models Based on Attention Mechanism
... different modal data are often used to describe the same event or ...other modal resources (such as images, videos, ...is, cross-modal Retrieval. Cross-modal ... See full document
5
Aligning Multilingual Word Embeddings for Cross Modal Retrieval Task
... the retrieval task hasn’t seen enough number of ...the retrieval task tries to fine-tune word embed- ...the learning rate of retrieval task to improve the performance, and the alignment ratio ... See full document
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auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
... Finally, we compare the performance of auDeep with baseline and state-of-the-art approaches for the different datasets (cf. Figure 2, identified by authors’ names). We observe that auDeep either matches or outperforms a ... See full document
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Content based analysis retrieval using audiovisual archive retrieval with cross referencing and multimodal re ranking
... video retrieval can improve audiovisual archive ...how retrieval performance for professional searches is ...queries based on the information needs of users from the audiovisual archive, and their ... See full document
5
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
... More than half of the human brain is involved in visual processing. While it took mother nature bil- lions of years to evolve and deliver us a remarkable human visual system, computer vision is one of the youngest ... See full document
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Adversarial Learning of Semantic Relevance in Text to Image Synthesis
... CGAN is fundamental to many approaches for text-to-image synthesis. Conditioning gives a means to control the genera- tive process that the original GAN lacks. Reed et al. (2016b) were the first to propose the ... See full document
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Combining Low-Level Features for Semantic Extraction in Image Retrieval
... image retrieval ap- proach based on image blocks is ...given semantic object in an ...optimization based on a Pareto archived evolution strategy (PAES) ... See full document
12
On Evaluation of Adversarial Perturbations for Sequence to Sequence Models
... each adversarial input is at edit distance at most 3 from the original ...subword- based LSTM and Transformer ...graphical representation of these same results in Figure 1 for the word-based ... See full document
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Weakly Supervised Concept based Adversarial Learning for Cross lingual Word Embeddings
... Comparison with unsupervised GANs As we have mentioned before, the preliminary mappings trained by the method of Lample et al. (2018) per- form well for some similar language pairs, such as Spanish to English and French ... See full document
12
Robust Semantic Parsing with Adversarial Learning for Domain Generalization
... For many NLP applications, models that per- form well on multiple domains and data sources are essential. As data labeling is expensive and time consuming, especially when it requires spe- cific expertise (FrameNet, ... See full document
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Joint Representation Learning for Multi-Modal Transportation Recommendation
... The multi-modal transportation graph has unique character- istics. In the graph, there are only several (e.g., 5 in our case) transport mode nodes whereas there are a large num- ber of user nodes and OD nodes. A ... See full document
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