29 citations · 79 across the 10 of their papers we have counts for
6 papers · 1 filter
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to…
Metric Learning as a Service with Covariance Embedding
Imam Mustafa Kamal, Hyerim Bae, Ling Liu
With the emergence of deep learning, metric learning has gained significant popularity in numerous machine learning tasks dealing with complex and large-scale datasets, such as inf…
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineer…
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images
Zhongwei Xie, Ling Liu, Lin Li +1
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. T…
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning
Zhongwei Xie, Ling Liu, Lin Li +1
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 th…
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures.…