# Wide-Slice Residual Networks for Food Recognition

arXiv: https://arxiv.org/abs/1612.06543 · HTML: https://mlsanity.com/paper/1612.06543/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

## Built on by (most cited first, 18 of 18)

- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021)
- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [FIRE: Food Image to REcipe generation](https://mlsanity.com/paper/2308.14391/) (2023)
- [Mining Discriminative Food Regions for Accurate Food Recognition](https://mlsanity.com/paper/2207.03692/) (2022)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring](https://mlsanity.com/paper/2107.00372/) (2021)
- [Learn More for Food Recognition via Progressive Self-Distillation](https://mlsanity.com/paper/2303.05073/) (2023)
- [RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition](https://mlsanity.com/paper/2410.12718/) (2024)
- [Retrieval Augmented Recipe Generation](https://mlsanity.com/paper/2411.08715/) (2024)
- [Material Measurement Units for a Circular Economy: Foundations through a Review](https://mlsanity.com/paper/2103.01997/) (2021)
- [A Mobile Food Recognition System for Dietary Assessment](https://mlsanity.com/paper/2204.09432/) (2022)
- [GCAM: Gaussian and causal-attention model of food fine-grained recognition](https://mlsanity.com/paper/2403.12109/) (2024)
- [Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment](https://mlsanity.com/paper/2407.18854/) (2024)
- [Shape-Preserving Generation of Food Images for Automatic Dietary Assessment](https://mlsanity.com/paper/2408.13358/) (2024)
- [Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review](https://mlsanity.com/paper/2504.10527/) (2025)
- [Calorie Aware Automatic Meal Kit Generation from an Image](https://mlsanity.com/paper/2112.09839/) (2021)

## Abstract (arXiv)

Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. Specifically, inspired by the recent success of residual deep network, we exploit such a learning scheme and introduce a slice convolution block to capture the vertical food layers. Outputs of the deep residual blocks are combined with the sliced convolution to produce the classification score for specific food categories. To evaluate our proposed architecture we have conducted experimental results on three benchmark datasets. Results demonstrate that our solution shows better performance with respect to existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).