Automated Image Recognition Framework

2025-06-23

AI Summary

To address data scarcity and high annotation costs in image recognition tasks involving novel or sensitive topics, this paper proposes AIR, an automated image recognition framework. AIR integrates generative AI with large language models (LLMs), introducing two key innovations: (1) an automated prompt engineering module that tailors prompts to target domains, and (2) a distribution-aware deduplication and denoising algorithm to ensure high fidelity and diversity of synthetic data. It implements a dual-path mechanism—AIR-Gen for zero-shot synthetic image generation and AIR-Aug for augmentation-driven data expansion—supporting heterogeneous data supply scenarios. The framework enables end-to-end synthesis of high-quality pre-annotated training data and automatic training of deep classification models. Experiments demonstrate that models trained solely on AIR-generated data achieve performance comparable to those trained on real-world datasets. A user study yields a mean rating of 4.4/5.0, confirming AIR’s effectiveness and practical utility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Diffusion Models for Vision

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

While the efficacy of deep learning models heavily relies on data, gathering and annotating data for specific tasks, particularly when addressing novel or sensitive subjects lacking relevant datasets, poses significant time and resource challenges. In response to this, we propose a novel Automated Image Recognition (AIR) framework that harnesses the power of generative AI. AIR empowers end-users to synthesize high-quality, pre-annotated datasets, eliminating the necessity for manual labeling. It also automatically trains deep learning models on the generated datasets with robust image recognition performance. Our framework includes two main data synthesis processes, AIR-Gen and AIR-Aug. The AIR-Gen enables end-users to seamlessly generate datasets tailored to their specifications. To improve image quality, we introduce a novel automated prompt engineering module that leverages the capabilities of large language models. We also introduce a distribution adjustment algorithm to eliminate duplicates and outliers, enhancing the robustness and reliability of generated datasets. On the other hand, the AIR-Aug enhances a given dataset, thereby improving the performance of deep classifier models. AIR-Aug is particularly beneficial when users have limited data for specific tasks. Through comprehensive experiments, we demonstrated the efficacy of our generated data in training deep learning models and showcased the system's potential to provide image recognition models for a wide range of objects. We also conducted a user study that achieved an impressive score of 4.4 out of 5.0, underscoring the AI community's positive perception of AIR.

Problem

• Automates dataset creation for image recognition tasks
• Reduces manual labeling with generative AI synthesis
• Enhances model performance with limited task-specific data

Innovation

• Generative AI synthesizes pre-annotated datasets automatically
• Automated prompt engineering enhances image quality
• Distribution adjustment algorithm improves dataset robustness