VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders

• URL: http://arxiv.org/abs/2309.01141v4
• Date: Tue, 23 Jan 2024 15:51:18 GMT
• Title: VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
• Authors: Xuyang Liu, Siteng Huang, Yachen Kang, Honggang Chen, Donglin Wang
• Abstract summary: VGDiffZero is a zero-shot visual grounding framework based on text-to-image diffusion models. We show that VGDiffZero achieves strong performance on zero-shot visual grounding.
• Score: 31.371338262371122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training. However, most vision-language discriminative tasks require extensive fine-tuning on carefully-labeled datasets to acquire such alignment, with great cost in time and computing resources. In this work, we explore directly applying a pre-trained generative diffusion model to the challenging discriminative task of visual grounding without any fine-tuning and additional training dataset. Specifically, we propose VGDiffZero, a simple yet effective zero-shot visual grounding framework based on text-to-image diffusion models. We also design a comprehensive region-scoring method considering both global and local contexts of each isolated proposal. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg show that VGDiffZero achieves strong performance on zero-shot visual grounding. Our code is available at https://github.com/xuyang-liu16/VGDiffZero.

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