Latent Embeddings for Zero-shot Classification (1603.08895v2)

Abstract: We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it learns a collection of maps with the selection, of which map to use, being a latent variable for the current image-class pair. We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image. We empirically demonstrate that our model improves the state-of-the-art for various class embeddings consistently on three challenging publicly available datasets for the zero-shot setting. Moreover, our method leads to visually highly interpretable results with clear clusters of different fine-grained object properties that correspond to different latent variable maps.

• The paper presents a latent variable model that selects from multiple bilinear mappings to improve zero-shot classification accuracy.
• It employs a ranking-based SGD and a novel pruning strategy to optimize and fine-tune the latent mappings.
• Empirical evaluations on CUB, AWA, and Dogs datasets reveal significant accuracy gains over traditional methods.

Analyzing Latent Embeddings for Zero-shot Classification

The paper "Latent Embeddings for Zero-shot Classification" proposes a novel approach to zero-shot learning (ZSL) by introducing latent variables into the compatibility learning framework. The authors, Xian et al., extend state-of-the-art bilinear models by incorporating latent variables to learn a collection of compatibility mappings between image and class embeddings. This piecewise linear modeling is demonstrated to improve performance across various publicly available datasets.

Problem Context and Motivation

Zero-shot classification addresses the challenging task of predicting classes not seen during the training phase by leveraging secondary sources of information, such as attributes or textual descriptions. Given the inherent complexity and the fine-grained nature of many classification problems, learning a single bilinear map often proves insufficient. This limitation arises from the global linearity constraint, which inherently lacks the flexibility to capture nuances within the dataset.

Methodology

The authors propose a Latent Embedding (LatEm) model featuring latent variables that determine which among multiple bilinear maps should be employed for a given image-class pair. Specifically, the method operates as follows:

1. Latent Variable Model: Instead of a single bilinear compatibility function $x^T W y$, LatEm introduces multiple mappings $x^T W_i y$, where the selection of $W_i$ is conditioned on the image-class pair.
2. Optimization: The compatibility function is optimized using a ranking-based SGD method that dynamically selects and updates the most appropriate bilinear map $W_i$ during training.
3. Model Selection: Two strategies are put forward: cross-validation and pruning. The latter, a novel technique introduced in this work, entails starting with a large set of latent variables and progressively trimming those which are infrequently chosen.

Results and Contributions

The empirical evaluation is rigorous, encompassing three diverse datasets: CUB, AWA, and Dogs, demonstrating clear improvements over the baseline methods in terms of zero-shot learning accuracy:

• CUB Dataset: Significant improvements with a jump from 28.4% to 31.8% using word2vec embeddings.
• AWA Dataset: Marked increase from 51.2% to 61.1% using word2vec embeddings.
• Dogs Dataset: Gains from 19.6% to 22.6% using word2vec embeddings.

The incorporation of LatEm leads to visually interpretable clusters, supporting the hypothesis that different visual properties (e.g., color, beak shape in birds) are being effectively captured by different $W_i$'s.

The authors also provide an extensive comparison with other state-of-the-art methods. For supervised attribute embeddings, LatEm delivers superior results particularly with the AWA dataset (up to 71.9% from the previous 66.7%). Moreover, when combining multiple unsupervised embeddings, LatEm consistently outperformed existing methods across all datasets.

Implications and Future Directions

From a practical standpoint, LatEm's ability to leverage multiple mappings makes it a flexible and powerful tool for applications requiring fine-grained image classification. The consistent performance gains across different datasets suggest robustness and potentially wide applicability.

Theoretically, the introduction of latent variables within the compatibility learning framework opens up new avenues for more sophisticated models in zero-shot and related learning tasks. Future research could explore further refinements in latent variable selection and optimization techniques.

Moreover, while LatEm performs admirably with both supervised and unsupervised embeddings, future advancements might involve hybrid models combining multiple sources of side information in more sophisticated ways, potentially leveraging deep learning techniques to refine both image and class embeddings.

In conclusion, "Latent Embeddings for Zero-shot Classification" presents a substantial step forward in the field, offering a methodologically sound, empirically validated approach that sets a new benchmark for what can be achieved in zero-shot learning tasks.