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This paper describes our solution to the multi-modal learning challenge ofICML. This solution comprises constructing three-level representations in threeconsecutive stages and choosing correct tag words with a data-specificstrategy. Firstly, we use typical methods to obtain level-1 representations.Each image is represented using MPEG-7 and gist descriptors with additionalfeatures released by the contest organizers. And the corresponding word tagsare represented by bag-of-words model with a dictionary of 4000 words.Secondly, we learn the level-2 representations using two stacked RBMs for eachmodality. Thirdly, we propose a bimodal auto-encoder to learn thesimilarities/dissimilarities between the pairwise image-tags as level-3representations. Finally, during the test phase, based on one observation ofthe dataset, we come up with a data-specific strategy to choose the correct tagwords leading to a leap of an improved overall performance. Our final averageaccuracy on the private test set is 100%, which ranks the first place in thischallenge.

Constructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative Choice