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

by   Guo-Jun Qi, et al.
University of Central Florida

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.


The Benefits of Label-Description Training for Zero-Shot Text Classification

Large language models have improved zero-shot text classification by all...

UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning

Animal visual perception is an important technique for automatically mon...

Self-Training Ensemble Networks for Zero-Shot Image Recognition

Despite the advancement of supervised image recognition algorithms, thei...

Learning from Multiple Noisy Partial Labelers

Programmatic weak supervision creates models without hand-labeled traini...

Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Deep neural models have hitherto achieved significant performances on nu...

Structure propagation for zero-shot learning

The key of zero-shot learning (ZSL) is how to find the information trans...

Please sign up or login with your details

Forgot password? Click here to reset