# IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits

arXiv: https://arxiv.org/abs/2412.16807 · HTML: https://mlsanity.com/paper/2412.16807/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Food Recommendation: Framework, Existing Solutions and Challenges](https://mlsanity.com/paper/1905.06269/) (2019)

## Built on by (most cited first, 5 of 5)

- [Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets](https://mlsanity.com/paper/2506.09851/) (2025)
- [ViMoNet: A Multimodal Vision-Language Framework for Human Behavior Understanding from Motion and Video](https://mlsanity.com/paper/2508.09818/) (2025)
- [GANDiff FR: Hybrid GAN Diffusion Synthesis for Causal Bias Attribution in Face Recognition](https://mlsanity.com/paper/2508.11334/) (2025)
- [Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengali](https://mlsanity.com/paper/2601.17764/) (2026)
- [Pattern Recognition Tasks with Personalized Federated Learning](https://mlsanity.com/paper/2605.27816/) (2026)

## Abstract (arXiv)

Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract different attributes from the environmental images. Specifically, we identify five key attributes and employ an ensemble model IMVB7 based on five distinct models for some of their detection resulted 0.85 mark. In addition, we conducted surveys to discern patterns in food preferences in response to visual stimuli. Leveraging the insights gleaned from these surveys, we formulate recommendations using decision tree for dishes based on the amalgamation of identified attributes resulted IMVB7t 0.96 mark. This study serves as a foundational step, paving the way for further exploration of this interdisciplinary domain.