Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallow-structured models, deep models present superiority in capturing multimodal correlations due to their high nonlinearity. Hover, in order to make the learned representation more accurate and compact, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. ,  propose a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra-modality and inter-modality correlations. In particular, to reduce redundant information,  impose orthogonal regularizer on the ighting matrices of the model, and theoretically prove that the learned representation is guaranteed to be approximately orthogonal. Moreover,  find that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities. Comprehensive experiments on WIKI and NUS-WIDE, demonstrate a substantial gain of DMHOR compared with state-of-the-art methods.
['Daixin Wang', 'Peng Cui', 'Mingdong Ou', 'Wenwu Zhu']
I am writing a research paper on a idea Deep multimodal hashing with orthogonal regularization
[{"from": "human", "value": "I am writing a research paper on a idea Deep multimodal hashing with orthogonal regularization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallow-structured models, deep models present superiority in capturing multimodal correlations due to their high nonlinearity. Hover, in order to make the learned representation more accurate and compact, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. ,  propose a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra-modality and inter-modality correlations. In particular, to reduce redundant information,  impose orthogonal regularizer on the ighting matrices of the model, and theoretically prove that the learned representation is guaranteed to be approximately orthogonal. Moreover,  find that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities. Comprehensive experiments on WIKI and NUS-WIDE, demonstrate a substantial gain of DMHOR compared with state-of-the-art methods."}]