From Zero to Hero: Shaking Up the Field of Zero-shot Learning

This article is part of the Academic Alibaba series and is taken from the paper entitled “Transductive Unbiased Embedding for Zero-Shot Learning” by Jie Song, Chengchao Shen, and Mingli Song, Yezhou Yang and Yang Liu. The full paper can be read here.

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Avoiding Bias

One issue that affects many ZSL approaches is bias. Because the collection and labeling of training data is both labor-intensive and expensive, and because it remains difficult to gather enough statistically diverse training images (particularly for rare categories, such as an endangered species), unlabeled target classes (i.e. images or objects that have not been seen before), are often categorized as labeled source classes, which results in a poor performance in generalized settings.

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Bias towards source classes in a semantic embedding space

QFSL — New Solution

To resolve this issue, the Alibaba tech team has developed a straightforward and effective transductive method — quasi-fully supervised learning, or QFSL — which assumes that both the labeled source data and the unlabeled target data are available during the training.

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Overall architecture of the QFSL model

Looking Ahead

When tested against various benchmarks, QFSL outperformed existing state-of-the-art ZSL methods by a wide margin in both generalized and conventional settings.

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The best result is marked in bold, with second best in blue: QFSL clearly outperforms other methods
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