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In this paper, we present a label transfer model from texts to images forimage classification tasks. The problem of image classification is often muchmore challenging than text classification. On one hand, labeled text data ismore widely available than the labeled images for classification tasks. On theother hand, text data tends to have natural semantic interpretability, and theyare often more directly related to class labels. On the contrary, the imagefeatures are not directly related to concepts inherent in class labels. One ofour goals in this paper is to develop a model for revealing the functionalrelationships between text and image features as to directly transferintermodal and intramodal labels to annotate the images. This is implemented bylearning a transfer function as a bridge to propagate the labels between twomultimodal spaces. However, the intermodal label transfers could be underminedby blindly transferring the labels of noisy texts to annotate images. Tomitigate this problem, we present an intramodal label transfer process, whichcomplements the intermodal label transfer by transferring the image labelsinstead when relevant text is absent from the source corpus. In addition, wegeneralize the inter-modal label transfer to zero-shot learning scenario wherethere are only text examples available to label unseen classes of imageswithout any positive image examples. We evaluate our algorithm on an imageclassification task and show the effectiveness with respect to the othercompared algorithms.

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes