# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 2 Related Work
### Zero-Shot Learning and Zero-Shot Detection  
Zero-Shot Learning (ZSL) enables the recognition of categories not seen during training by establishing a mapping between visual features and semantic descriptions [7]. Traditional ZSL methods can be broadly categorized into two approaches: mapping-based and generation-based.  
Mapping-based methods learn a projection between visual and semantic spaces, allowing the model to classify unseen categories based on their semantic attributes [22; 23].  
For example, [PERSON] et al. [PERSON] et al. (2015) proposed Direct Attribute Prediction (DAP), which uses attribute-based descriptions to recognize unseen classes. [PERSON] et al. [PERSON] et al. (2015) introduced DeviSE, which leverages word embeddings to enable zero-shot recognition. However, these methods typically suffer from the domain shift problem, where the projection learned from the seen classes generalizes poorly to unseen ones ([PERSON] et al., 2016).  
Generation-based methods address this limitation by synthesizing visual features for unseen classes based on their semantic descriptions, effectively transforming ZSL into a supervised learning problem ([PERSON] et al., 2016; [PERSON] et al., 2017). [PERSON] et al. [PERSON] et al. (2017) proposed f-VAEGAN-D2, which uses a conditional VAE-GAN architecture to generate features for unseen classes. [PERSON] et al. [PERSON] et al. (2017) introduced CADA-VAE, which aligns visual and semantic distributions in a shared latent space to improve feature generation.  
ZSD extends ZSL to the object detection domain, enabling models to simultaneously localize and classify objects from unseen categories ([PERSON] et al., 2016). [PERSON] et al. [PERSON] et al. (2016) pioneered ZSD by adapting existing detection frameworks to incorporate semantic information. Subsequent works have explored various approaches to improve ZSD performance. [PERSON] et al. [PERSON] et al. (2017) proposed a polarity loss to better align visual and semantic features, while [PERSON] et al. [PERSON] et al. (2017) introduced a feature generation approach that synthesizes region features for unseen classes.  
More recently, generation-based methods have shown superior performance in ZSD. [PERSON] et al. [PERSON] et al. (2017) proposed a GAN-based framework that synthesizes diverse features for unseen classes, while [PERSON] et al. [PERSON] et al. (2017) introduced a feature synthesizer that preserves the structural relationships between classes. However, these methods typically rely on generic word embeddings or simple attribute vectors, limiting their effectiveness for fine-grained zero-shot tasks with complex class relationships, such as food safety hazard detection.  
Our work builds upon generation-based ZSD but introduces a novel knowledge-enhanced feature synthesis approach specifically designed for the food safety domain. By leveraging rich domain knowledge encoded in a specialized food safety knowledge graph, our method can capture complex relationships between food categories and visual safety attributes, enabling the more effective zero-shot detection of food safety hazards.