Recognize after early fusion: the Chinese food recognition based on the alignment of image and ingredients

Zhang, Ruoxuan; Ouyang, Dantong; He, Lili; Kuang, Lingjin; Bai, Hongtao

doi:10.1007/s00530-024-01297-w

Multimedia Systems

2024

DOI: 10.1007/s00530-024-01297-w

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Recognize after early fusion: the Chinese food recognition based on the alignment of image and ingredients

Ruoxuan Zhang,

Dantong Ouyang,

Lili He

et al.

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GCAM: Gaussian and causal-attention model of food fine-grained recognition

Zhuang,

Hu,

Yan

et al. 2024

SIViP

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Currently, most food recognition relies on deep learning for category classification. However, these approaches struggle to effectively distinguish between visually similar food samples, highlighting the pressing need to address fine-grained issues in food recognition. To address these issues, we advocate for a Gaussian and causal-attention model specifically designed for nuanced object recognition. This model involves training to capture Gaussian characteristics in targeted areas, followed by extracting detailed features from the objects, thus improving the target regions' feature mapping capabilities. To counter data drift caused by skewed data distributions, we implement a counterfactual reasoning strategy. Through counterfactual interventions, the effect of the learned image attention mechanism on network predictions is examined, allowing for the optimization of attention weights in detailed image recognition. A learnable loss strategy is also developed to ensure consistent training across various modules, thereby enhancing the precision of the ultimate recognition task. Our method has been validated on four pertinent datasets, where it demonstrated superior performance. Specifically, the Gaussian and Causal-Attention Model (GCAM) has outperformed existing stateof-the-art methods on the ETH-FOOD101, UECFOOD256, and Vireo-FOOD172 datasets and achieved leading results on the CUB-200 dataset.

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GCAM: Gaussian and causal-attention model of food fine-grained recognition

Zhuang,

Hu,

Yan

et al. 2024

SIViP

View full text Add to dashboard Cite

show abstract

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Recognize after early fusion: the Chinese food recognition based on the alignment of image and ingredients

Cited by 1 publication

References 47 publications

GCAM: Gaussian and causal-attention model of food fine-grained recognition

GCAM: Gaussian and causal-attention model of food fine-grained recognition

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