  Food detection is becoming a fundamental task in food computing that supports
various multimedia applications, including food recommendation and dietary
monitoring. To deal with real-world scenarios, food detection needs to localize
and recognize novel food objects that are not seen during training, demanding
Zero-Shot Detection (ZSD). However, the complexity of semantic attributes and
intra-class feature diversity poses challenges for ZSD methods in
distinguishing fine-grained food classes. To tackle this, we propose the
Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food
Detection (ZSFD). SeeDS consists of two modules: a Semantic Separable
Synthesizing Module (S$^3$M) and a Region Feature Denoising Diffusion Model
(RFDDM). The S$^3$M learns the disentangled semantic representation for complex
food attributes from ingredients and cuisines, and synthesizes discriminative
food features via enhanced semantic information. The RFDDM utilizes a novel
diffusion model to generate diversified region features and enhances ZSFD via
fine-grained synthesized features. Extensive experiments show the
state-of-the-art ZSFD performance of our proposed method on two food datasets,
ZSFooD and UECFOOD-256. Moreover, SeeDS also maintains effectiveness on general
ZSD datasets, PASCAL VOC and MS COCO. The code and dataset can be found at
https://github.com/LanceZPF/SeeDS.
