Semantics Disentangling for Generalized Zero-Shot Learning (2101.07978v5)

Abstract: Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and unseen classes, most GZSL methods attempt to associate the visual features of seen classes with attributes or to generate unseen samples directly. Nevertheless, the visual features used in the prior approaches do not necessarily encode semantically related information that the shared attributes refer to, which degrades the model generalization to unseen classes. To address this issue, in this paper, we propose a novel semantics disentangling framework for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen classes are firstly estimated by a conditional VAE and then factorized into semantic-consistent and semantic-unrelated latent vectors. In particular, a total correlation penalty is applied to guarantee the independence between the two factorized representations, and the semantic consistency of which is measured by the derived relation network. Extensive experiments conducted on four GZSL benchmark datasets have evidenced that the semantic-consistent features disentangled by the proposed SDGZSL are more generalizable in tasks of canonical and generalized zero-shot learning. Our source code is available at https://github.com/uqzhichen/SDGZSL.

• The paper presents SDGZSL, a novel framework that factorizes visual features into semantic-consistent and unrelated latent vectors to better classify unseen classes.
• It achieves superior performance, including a 34.4% harmonic mean on the AWA dataset, by reducing misclassification rates among similar categories.
• The approach offers both theoretical insights and practical implications, setting a foundation for improved visual-semantic models and broader zero-shot learning applications.

Semantics Disentangling for Generalized Zero-Shot Learning: A Technical Overview

The paper "Semantics Disentangling for Generalized Zero-Shot Learning" presents an innovative approach to tackle the challenges of generalized zero-shot learning (GZSL) by proposing a new framework called Semantics Disentangling GZSL (SDGZSL). The focus of this research is on improving the generalization capabilities of models when faced with the task of classifying samples from unseen classes, a typical GZSL problem.

Core Contributions

The conventional approaches to GZSL often rely on associating visual features from seen classes with shared attributes or directly generating samples for unseen classes. However, these methods have limitations due to the inadequacy of visual features in encoding the semantic information necessary for making robust predictions about unseen classes. This paper circumvents these obstacles by introducing a novel semantics disentangling framework that leverages conditional variational autoencoders (VAEs).

The heart of the proposed SDGZSL method lies in its capacity to factorize visual features into two latent vectors: one that is semantic-consistent and another that is semantic-unrelated. The notion is to disentangle attributes directly related to the semantics necessary for classifying unseen classes from those that are not. This is achieved by applying a total correlation penalty to ensure independence between these two vectors, enhancing the semantic relevance of the transformed representations. A relation network is specifically devised to measure the semantic consistency of the output.

Experimental Evaluation and Results

The authors validate their approach through extensive experimentation across four benchmark datasets for GZSL: aPaY, AWA, CUB, and FLO. The SDGZSL framework consistently outperforms existing state-of-the-art methods by a considerable margin in terms of accuracy. For instance, in the AWA dataset, the method achieves a harmonic mean of unseen and seen class accuracies of 34.4%, outperforming traditional methods such as ALE and Devise.

Moreover, the class-wise analysis presented in the paper emphasizes the model's ability to reduce the misclassification rates between visually similar categories, a common issue in GZSL tasks. This improvement demonstrates the potential of the disentangled semantic-consistent representations in enhancing model robustness.

Theoretical and Practical Implications

The proposed SDGZSL framework has substantial implications both theoretically and practically. Theoretically, it provides a structured method of disentangling semantic information within the learned representations, which could be explored further in other related domains, such as few-shot learning or incremental learning. Practically, the insights from this research may guide the development of more effective visual-semantic models that are capable of generalized knowledge transfer, which is crucial for building adaptable AI systems.

Speculation on Future Developments

The advancement presented in this paper sets the stage for future research in several directions. Firstly, exploring the applicability of semantics disentangling in other forms of zero-shot learning tasks, not limited to visual domains, could yield interesting developments. Furthermore, integrating this framework with other advanced generative models, such as GANs or more complex VAEs, might enhance the quality of generated unseen class samples, further boosting model performance. Additionally, tackling hard classes that remain challenging for the proposed framework could involve innovative modifications or hybrid approaches integrating multiple learning paradigms.

In summary, this paper contributes substantially to the field of generalized zero-shot learning by proposing an effective semantics disentangling framework, evidenced by superior experimental results and rich theoretical implications. This work paves the way for novel approaches in the broader landscape of semantic understanding in AI.