Synthesized Classifiers for Zero-Shot Learning (1603.00550v3)

Abstract: Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main idea is to align the semantic space that is derived from external information to the model space that concerns itself with recognizing visual features. To this end, we introduce a set of "phantom" object classes whose coordinates live in both the semantic space and the model space. Serving as bases in a dictionary, they can be optimized from labeled data such that the synthesized real object classifiers achieve optimal discriminative performance. We demonstrate superior accuracy of our approach over the state of the art on four benchmark datasets for zero-shot learning, including the full ImageNet Fall 2011 dataset with more than 20,000 unseen classes.

• The paper introduces a novel manifold learning framework for zero-shot learning that employs phantom classes to synthesize classifiers for both seen and unseen classes.
• It formulates classifier synthesis as a convex combination of phantom class projections, significantly reducing learning costs while preserving semantic relationships.
• Extensive experiments on benchmarks like AwA, CUB, and ImageNet demonstrate the method's superior accuracy compared to state-of-the-art zero-shot learning approaches.

Synthesized Classifiers for Zero-Shot Learning

"Synthesized Classifiers for Zero-Shot Learning," authored by Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha, addresses the challenges inherent in zero-shot learning (ZSL) by proposing an innovative approach using manifold learning.

Zero-shot learning seeks to recognize objects from unseen classes that have no examples available during training. The fundamental challenges are establishing the relationship between seen and unseen classes and achieving optimal discriminative performance on these unseen classes. The authors propose a unique methodology to tackle these challenges, leveraging manifold learning principles and introducing "phantom" object classes.

The core idea is to align the semantic space, derived from external information such as attributes and word vectors, with the model space used for recognizing visual features. To achieve this, the authors introduce phantom classes that serve as intermediary bases shared between both seen and unseen classes. This process involves creating a weighted bipartite graph in the semantic space and ensuring the model space coordinates (classifiers) are projections of these vertices from the semantic space. The projection maintains the graph structure defined by the weights, which reflect how classes are semantically related.

The synthesis of classifiers for real object classes from these phantom classes is a crucial aspect. The real classifier for a class is constructed as a convex combination of the classifiers for the phantom classes, with the combination coefficients calculated based on semantic similarities. This approach not only allows the construction of an infinite number of classifiers but also significantly reduces learning costs by constraining the number of base classifiers.

Key Contributions

1. Novel Manifold Learning Application: The authors conceptualize zero-shot learning as a manifold learning problem, where a graph-based representation of class embeddings is aligned to the model space.
2. Phantom Classes: Introduction of phantom classes that do not correspond to actual objects but serve as bases for constructing classifiers for both seen and unseen classes using convex combinations.
3. Optimization Scheme: An efficient optimization scheme to learn optimal phantom class embeddings and classifiers, ensuring minimal error in synthesizing classifiers.

Experimental Results

The approach was validated using multiple benchmark datasets for zero-shot learning, including AwA, CUB, SUN, and the full ImageNet 2011 dataset comprising more than 20,000 unseen classes. The results demonstrate superior accuracy over state-of-the-art methods across various settings.

Implications and Future Work

The practical implications of this research are significant in fields requiring fine-grained object recognition from limited or no prior examples, such as automated taxonomy and real-time object detection in dynamic environments. Theoretically, this approach provides a robust framework for further exploration of manifold learning in high-dimensional spaces for complex recognition tasks.

Future developments may include enhancing the semantic similarity metrics, exploring non-linear kernel extensions of the synthesis process, and incorporating more adaptive and automated methods for determining the optimal number of base classifiers. The methodology also sets the stage for advancing zero-shot learning in settings with more complex relationships and higher-dimensional semantic spaces.

Conclusion

The paper "Synthesized Classifiers for Zero-Shot Learning" by Changpinyo et al. advances the field of zero-shot learning through a unique application of manifold learning principles and the innovative concept of phantom classes. The approach significantly improves recognition accuracy on unseen classes, making a substantial contribution to the field and opening avenues for further research and practical applications.