  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 mitigate these challenges,
we propose the adoption of a Gaussian and causal-attention model for
fine-grained object recognition.In particular, we train to obtain Gaussian
features over target regions, followed by the extraction of fine-grained
features from the objects, thereby enhancing the feature mapping capabilities
of the target regions. To counteract data drift resulting from uneven data
distributions, we employ a counterfactual reasoning approach. By using
counterfactual interventions, we analyze the impact of the learned image
attention mechanism on network predictions, enabling the network to acquire
more useful attention weights for fine-grained image recognition. Finally, we
design a learnable loss strategy to balance training stability across various
modules, ultimately improving the accuracy of the final target recognition. We
validate our approach on four relevant datasets, demonstrating its excellent
performance across these four datasets.We experimentally show that GCAM
surpasses state-of-the-art methods on the ETH-FOOD101, UECFOOD256, and
Vireo-FOOD172 datasets. Furthermore, our approach also achieves
state-of-the-art performance on the CUB-200 dataset.
