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Found 6 papers, 2 papers with code
Global and Local Semantic Completion Learning for Vision-Language Pre-training
MGSC promotes learning more representative global features, which have a great impact on the performance of downstream tasks, while MLTC reconstructs modal-fusion local tokens, further enhancing accurate comprehension of multimodal data.
Unsupervised Hashing with Semantic Concept Mining
Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model.
Deep Cross-modal Hashing via Margin-dynamic-softmax Loss
Finally, by minimizing the novel \textit{margin-dynamic-softmax loss}, the modality-specific hashing networks can be trained to generate hash codes which can simultaneously preserve the cross-modal similarity and abundant semantic information well.
Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning
Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs.
Object Detection based Deep Unsupervised Hashing
Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval.
