TransZero: Attribute-guided Transformer for Zero-Shot Learning (2112.01683v1)

Abstract: Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region features, enabling significant visual-semantic interaction. Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network, termed TransZero, to refine visual features and learn attribute localization for discriminative visual embedding representations in ZSL. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks, and improves the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the image regions most relevant to each attribute in a given image, under the guidance of semantic attribute information. Then, the locality-augmented visual features and semantic vectors are used to conduct effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves the new state of the art on three ZSL benchmarks. The codes are available at: \url{https://github.com/shiming-chen/TransZero}.

TransZero: Attribute-guided Transformer for Zero-Shot Learning

The research paper introduces TransZero, an innovative approach to tackle the challenges of zero-shot learning (ZSL) by utilizing an attribute-guided Transformer network. ZSL is a method that aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones through shared attributes. Recent methods in attention-based models have attempted to learn region features within a single image but have commonly overlooked the transferability and discriminative attribute localization necessary for effective ZSL.

TransZero is designed to refine visual features and enhance attribute localization, facilitating discriminative visual embedding representations. It incorporates a feature augmentation encoder that mitigates cross-dataset biases between datasets such as ImageNet and specific ZSL benchmarks. Furthermore, TransZero reduces entangled geometric relationships among region features, improving the transferability of visual features.

The paper's methodology introduces a visual-semantic decoder that localizes image regions relevant to each attribute using semantic attribute guidance. This locality-augmented approach ensures effective visual-semantic interaction within the visual-semantic embedding network. Such interactions and embedding strategies have proven crucial in achieving robust zero-shot learning outcomes.

Empirical evaluations across three benchmarks—CUB, SUN, and AWA2—demonstrate that TransZero consistently achieves state-of-the-art results. Importantly, attention is drawn to the robust performance improvements on both seen and unseen classes, thanks to the visual feature refinement and self-calibration techniques that reduce bias and enhance generalizability.

Implications and Future Directions

The paper provides compelling evidence for the efficacy of the TransZero model in ZSL tasks, suggesting broader implications for its integration into visual and language understanding systems. By refining locality-augmented visual features, TransZero offers an approach that can be extended to various domains requiring fine-grained semantic attribute learning.

Future advancements may look into the scalability of TransZero, expanding its applicability across more complex and diversified datasets. Moreover, the approach may inspire further research into leveraging transformers within ZSL, optimizing self-supervised learning strategies, and potentially adapting TransZero methodologies in related fields such as visual-linguistic reasoning or cross-domain learning frameworks.

In summary, the paper extends the application of transformers into zero-shot learning, showing promising results that pave the way for advancements in effective knowledge transfer and semantic attribute localization.