# Privacy Preserving Ordinal-Meta Learning with VLMs for Fine-Grained Fruit Quality Prediction

arXiv: https://arxiv.org/abs/2511.01449 · HTML: https://mlsanity.com/paper/2511.01449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token](https://mlsanity.com/paper/2501.03895/) (2025)
- [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://mlsanity.com/paper/2111.08851/) (2021)
- [Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models](https://mlsanity.com/paper/2306.02080/) (2023)
- [Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning](https://mlsanity.com/paper/2302.14794/) (2023)
- [A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs](https://mlsanity.com/paper/2410.18779/) (2024)
- [Fruit Ripeness Classification: a Survey](https://mlsanity.com/paper/2212.14441/) (2022)
- [Meta ordinal weighting net for improving lung nodule classification](https://mlsanity.com/paper/2102.00456/) (2021)
- [Facilitated machine learning for image-based fruit quality assessment](https://mlsanity.com/paper/2207.04523/) (2022)
- [Meta-learning For Vision-and-language Cross-lingual Transfer](https://mlsanity.com/paper/2305.14843/) (2023)

## Abstract (arXiv)

To effectively manage the wastage of perishable fruits, it is crucial to accurately predict their freshness or shelf life using non-invasive methods that rely on visual data. In this regard, deep learning techniques can offer a viable solution. However, obtaining fine-grained fruit freshness labels from experts is costly, leading to a scarcity of data. Closed proprietary Vision Language Models (VLMs), such as Gemini, have demonstrated strong performance in fruit freshness detection task in both zero-shot and few-shot settings. Nonetheless, food retail organizations are unable to utilize these proprietary models due to concerns related to data privacy, while existing open-source VLMs yield sub-optimal performance for the task. Fine-tuning these open-source models with limited data fails to achieve the performance levels of proprietary models. In this work, we introduce a Model-Agnostic Ordinal Meta-Learning (MAOML) algorithm, designed to train smaller VLMs. This approach utilizes meta-learning to address data sparsity and leverages label ordinality, thereby achieving state-of-the-art performance in the fruit freshness classification task under both zero-shot and few-shot settings. Our method achieves an industry-standard accuracy of 92.71%, averaged across all fruits. Keywords: Fruit Quality Prediction, Vision Language Models, Meta Learning, Ordinal Regression