Zero-Shot Learning via Semantic Similarity Embedding (1509.04767v2)

Abstract: In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (\eg attributes) for unseen classes. Our method is based on viewing each source or target data as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two instances belong to the same unseen class. This perspective leads us to learning source/target embedding functions that map an arbitrary source/target domain data into a same semantic space where similarity can be readily measured. We develop a max-margin framework to learn these similarity functions and jointly optimize parameters by means of cross validation. Our test results are compelling, leading to significant improvement in terms of accuracy on most benchmark datasets for zero-shot recognition.

• The paper introduces semantic similarity embedding functions to map both attribute and data domains into a unified space for effective zero-shot recognition.
• It employs a max-margin framework with cross-validation to derive embeddings, significantly improving accuracy on benchmarks like CIFAR-10 and aPascal & aYahoo.
• The approach broadens zero-shot learning applications by enabling scalable, robust recognition in large-scale, multi-class scenarios.

Zero-Shot Learning via Semantic Similarity Embedding

The paper "Zero-Shot Learning via Semantic Similarity Embedding" by Ziming Zhang and Venkatesh Saligrama presents a novel approach to addressing the zero-shot learning (ZSL) problem by leveraging semantic similarity embeddings. This work explores the challenge of recognizing instances from unseen classes by utilizing seen class data, focusing on embedding functions that map both source (attribute) and target (data) domain information into a shared semantic space. The authors propose a method to quantify similarity in this space, significantly improving recognition accuracy on several benchmark datasets.

Core Contribution

The authors introduce a concept of semantic similarity embedding (SSE) functions to transform data from both source and target domains into a common semantic space. The approach hypothesizes that if instances from source and target domains belong to the same unseen class, their mixture proportions in the semantic space should be similar. The SSE functions are derived using a max-margin framework, optimizing parameters through cross-validation.

Methodology

The methodology centers on expressing both source and target domains as proportions of seen classes:

• Source Domain Embedding: This is conceptualized through a parameterized optimization problem akin to sparse coding, aligning attribute vectors onto a simplex.
• Target Domain Embedding: The embedding utilizes class-dependent feature transformations. Two variants are proposed—Intersection function (INT) and Rectified Linear Unit (ReLU)—to optimize the embedding framework.

The semantic alignment is achieved by jointly optimizing seen class data, employing margin-based constraints to maintain coherence between distributions across domains.

Results

The experimental results highlight significant performance boosts on datasets such as CIFAR-10, aPascal & aYahoo, Animals with Attributes, CUB-200-2011, and SUN Attribute. Using deep features, the method outperformed existing approaches, demonstrating robust generalization, especially in large-scale and many-class scenarios.

Theoretical and Practical Implications

The key theoretical implication lies in the proposition that unseen class instances can be effectively represented using semantic affinities of seen classes. This shift in focus from individual instance classification to distributional alignment offers a fresh perspective in ZSL research.

Practically, the approach demonstrates scalability and adaptability to large-scale recognition tasks, broadening potential applications in areas such as activity retrieval and person re-identification.

Future Directions

The research opens several avenues for further exploration:

• Enhanced feature engineering to improve fine-grained recognition, potentially integrating additional attribute information or domain adaptation techniques.
• Extending the framework to other domains beyond image classification, such as text or multi-modal data.

In conclusion, Zhang and Saligrama provide a substantial contribution to zero-shot learning by harnessing semantic similarity embedding. Their proposed framework effectively bridges the gap between seen and unseen class recognition, setting a foundation for future research in scalable and transferable learning models.