QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain

TL;DR

QUOTA introduces a domain-agnostic, optimization-based framework for accurate object quantification in text-to-image models without retraining, effectively handling unseen domains and stylistic variations.

Contribution

It presents the first domain-agnostic, meta-learning approach for object quantification in text-to-image models, avoiding retraining and improving scalability.

Findings

Outperforms existing models in quantification accuracy

Maintains high accuracy across unseen domains

Sets new benchmarks in domain generalization for object counting

Abstract

We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which leads to high computational costs and limited scalability, we are the first to consider this problem from a domain-agnostic perspective. We propose QUOTA, an optimization framework for text-to-image models that enables effective object quantification across unseen domains without retraining. It leverages a dual-loop meta-learning strategy to optimize a domain-invariant prompt. Further, by integrating prompt learning with learnable counting and domain tokens, our method captures stylistic variations and maintains accuracy, even for object classes not encountered during training. For evaluation, we adopt a new benchmark specifically designed for object quantification in domain generalization, enabling rigorous assessment of object quantification accuracy and adaptability across unseen domains in text-to-image generation. Extensive experiments demonstrate that QUOTA outperforms conventional models in both object quantification accuracy and semantic consistency, setting a new benchmark for efficient and scalable text-to-image generation for any domain.