# Enabling Validation for Robust Few-Shot Recognition

arXiv: https://arxiv.org/abs/2506.04713 · HTML: https://mlsanity.com/paper/2506.04713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 70 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 30 of 63)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)

## Built on by (most cited first, 2 of 2)

- [Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective](https://mlsanity.com/paper/2512.10244/) (2025)
- [Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors](https://mlsanity.com/paper/2512.15748/) (2025)

## Abstract (arXiv)

A key challenge in Few-Shot Recognition (FSR) is the scarcity of labeled task-specific data for training. Contemporary methods mitigate this challenge by exploiting pretrained Vision-Language Models (VLMs) for their learned transferable features. Some recent ones augment the scarce data by retrieving relevant yet out-of-distribution (OOD) examples from external data sources. However, they largely overlook an equally fundamental challenge: the absence of validation data for model selection and hyperparameter tuning. As a result, the adapted VLMs overfit the few-shot training data and generalize poorly to OOD test data. We show that retrieved data provide a natural source of validation, but their direct use creates a fundamental paradox. Since the retrieved samples are inherently OOD w.r.t the few-shot ID training data, adapting a VLM to the ID data constantly degrades its performance on the retrieved data. Consequently, conventional validation relying on the accuracy of retrieved data favors no adaptation and prevents improved generalization. To resolve this dilemma, we formulate validation as a multi-objective optimization (MOO) problem between performance gains on the few-shot ID data and the performance preservation on the retrieved data. We introduce a simple yet effective validation strategy by choosing an operating point on the Pareto front of the MOO problem. This strategy enables hyperparameter tuning, which had been unrealistically done by using test data, thereby mitigating overfitting and improving generalization. We integrate it into a stage-wise finetuning pipeline, termed Validation-Enabled Stage-wise Tuning (VEST). Extensive experiments on five established benchmarks show that VEST consistently outperforms existing approaches on both ID and OOD test data.