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

arXiv: https://arxiv.org/abs/2403.12109 · HTML: https://mlsanity.com/paper/2403.12109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (2021)
- [Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches](https://mlsanity.com/paper/2003.03836/) (2020)
- [Learning Attentive Pairwise Interaction for Fine-Grained Classification](https://mlsanity.com/paper/2002.10191/) (2020)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification](https://mlsanity.com/paper/1901.09891/) (2019)
- [DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment](https://mlsanity.com/paper/1606.05675/) (2016)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers](https://mlsanity.com/paper/2303.18013/) (2023)

## Built on by (most cited first, 1 of 1)

- [MultiFoodhat: A potential new paradigm for intelligent food quality inspection](https://mlsanity.com/paper/2510.13889/) (2025)

## Abstract (arXiv)

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.