FREE: Feature Refinement for Generalized Zero-Shot Learning (2107.13807v1)

Abstract: Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over its baseline and current state-of-the-art methods. Our codes are available at https://github.com/shiming-chen/FREE .

• The paper introduces a feature refinement module that integrates semantic-to-visual mapping within a unified generative framework to address cross-dataset bias.
• It leverages SAMC-loss to enhance intra-class compactness and inter-class separability, yielding discriminative features for both seen and unseen classes.
• Experiments on five benchmark datasets show significant performance gains in harmonic mean scores over baseline methods.

An In-depth Analysis of Feature Refinement for Generalized Zero-Shot Learning

The paper "FREE: Feature Refinement for Generalized Zero-Shot Learning" presents a novel approach to tackle the fundamental challenges in generalized zero-shot learning (GZSL). This approach, termed FREE, aims to address the cross-dataset bias that occurs due to the use of pre-trained CNN backbone models like ResNet-101. This bias has been identified as limiting the quality of visual features for GZSL tasks, ultimately constraining recognition performances on both seen and unseen classes.

Key Contributions and Methodology

The authors propose a feature refinement module (FR) that integrates semantic-to-visual mapping into a unified generative framework. This framework leverages visual refining techniques and employs self-adaptive margin center loss (SAMC-loss) alongside semantic cycle-consistency loss to guide the FR module in learning class- and semantically-relevant representations.

Significant contributions include:

1. Addressing Cross-Dataset Bias: The FR module is collaboratively trained with a feature-generating VAEGAN to simultaneously enhance semantic-to-visual mapping, feature synthesis, and classification, by refining visual features of both seen and unseen classes.
2. Discriminative Feature Learning: The SAMC-loss encourages intra-class compactness and inter-class separability, assigning adaptive importance that adjusts based on the dataset’s granularity. This loss quantification allows FR to capture discriminative features that retain semantic relevance necessary for robust GZSL performance.
3. Unified Generative Model: By combining VAE and GAN, the approach synthesizes unseen class feature samples that are more aligned with their semantics, benefiting from the joint learning of the visual refinement and generative model.

Results and Implications

The model shows impressive performance gains across five benchmark datasets: CUB, SUN, FLO, AWA1, and AWA2. Specifically:

• Improvement Over Baseline: FREE demonstrated significant performance gains over its baseline, achieving competitive results on harmonic mean scores, indicating improved recognition of both seen and unseen classes.
• Generalization Across Data Granularity: It effectively enhances the classification on both fine-grained datasets where large cross-dataset bias exists and coarse-grained datasets. This suggests robust feature refinement capabilities that are not overly reliant on dataset type.

Future Directions

The methodological innovations presented in this paper open avenues for future research in several aspects of AI and machine learning:

1. Further Bias Mitigation Strategies: Investigating other facets of cross-dataset biases in conjunction with advanced techniques such as domain adaptation could yield improvements for GZSL.
2. Adoption in Other AI Fields: The FR module’s applicability could extend beyond vision applications to speech or text, particularly in cases requiring seamless generalization across unseen domains.
3. Hybrid Models: Exploration of hybrid models integrating more sophisticated structural constraints like graph-based approaches can enhance feature synthesis and class separation.

Conclusion

Overall, the paper contributes a substantial advancement in GZSL through its incorporation of feature refinement techniques in a unified framework, enhancing both theoretical understanding and practical application capabilities. The experiments illustrate the effectiveness of reducing cross-dataset bias, pointing towards promising future directions in AI generalization tasks.