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Found 7 papers, 4 papers with code
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model.
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images.
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
We present a Multi-modal Semantics enhanced Joint Embedding approach (MSJE) for learning a common feature space between the two modalities (text and image), with the ultimate goal of providing high-performance cross-modal retrieval services.
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning
This paper introduces a two-phase deep feature calibration framework for efficient learning of semantics enhanced text-image cross-modal joint embedding, which clearly separates the deep feature calibration in data preprocessing from training the joint embedding model.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy.
Promoting High Diversity Ensemble Learning with EnsembleBench
Ensemble learning is gaining renewed interests in recent years.
Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings
In this paper, we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information. Concretely speaking, cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space, where similarity can be directly computed.
