Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Supervised cross-modal hashing methods have achieved considerable progress by incorporating semantic side information. However, they mainly have two limitations: (1) Heavily rely on large-scale labeled cross-modal training data which are labor intensive and hard to obtain. (2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations. To address these problems, in this paper we propose a novel Semi-supervised Cross-Modal Hashing approach by Generative Adversarial Network (SCH-GAN). We aim to take advantage of GAN's ability for modeling data distributions to promote cross-modal hashing learning in an adversarial way. The main contributions can be summarized as follows: (1) We propose a novel generative adversarial network for cross-modal hashing. In our proposed SCH-GAN, the generative model tries to select margin examples of one modality from unlabeled data when giving a query of another modality. While the discriminative model tries to distinguish the selected examples and true positive examples of the query. These two models play a minimax game so that the generative model can promote the hashing performance of discriminative model. (2) We propose a reinforcement learning based algorithm to drive the training of proposed SCH-GAN. The generative model takes the correlation score predicted by discriminative model as a reward, and tries to select the examples close to the margin to promote discriminative model by maximizing the margin between positive and negative data. Experiments on 3 widely-used datasets verify the effectiveness of our proposed approach.
Supervised methods, especially supervised deep learning based methods have achieved promising results. However, supervised methods rely on large amount of labeled training data which are labor intensive to obtain. It is even harder to label cross-modal hashing training data, since multiple modalities are involved. Traditional semi-supervised learning methods @cite_18 can exploit unlabeled data effectively to help supervised training. Little efforts have been done for semi-supervised cross-modal hashing learning. In this paper, we attempt to fully exploit the unlabeled data to promote cross-modal hashing learning.
@cite_18
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Door lock apparatus in which a door latch mechanism is operated by inner and outer door handles coupled to a latch shaft extending through the latch mechanism. Handles are coupled to ends of latch shaft by coupling devices enabling door to be locked from the inside to prevent entry from the outside but can still be opened from the inside by normal operation of outside handle. Inside coupling device has limited lost-motion which is used to operate cam device to unlock the door on actuation of inner handles.