Feedback-Driven Vision-Language Alignment with Minimal Human Supervision

TL;DR

This paper presents SVP, a framework that improves vision-language model alignment using minimal human supervision by leveraging self-captioning and feedback, leading to significant performance gains across multiple tasks.

Contribution

Introduces SVP, a novel sampling-based framework that enhances vision-language alignment without extensive curated data or preference annotations.

Findings

14% average improvement in captioning tasks

Up to 12% increase in object recall

Significant hallucination reduction

Abstract

Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive, high-quality image-text training data. Curation of these image-text pairs is both time-consuming and computationally expensive. To address this challenge, we introduce SVP (Sampling-based Visual Projection), a novel framework that enhances vision-language alignment without relying on manually curated text-image pairs or preference annotation. SVP leverages a small set of manually selected images, self-captioning and a pre-trained grounding model as a feedback mechanism to elicit latent information in VLMs. We evaluate our approach across six key areas: captioning, referring, visual question answering, multitasking, hallucination control, and object recall. Results demonstrate significant improvements, including a 14 % average improvement in captioning tasks, up to 12 % increase in object recall, and significantly reduced hallucinations, while maintaining question-answering capabilities. Using SVP, a small VLM achieves hallucination reductions similar to a model five times larger, while a VLM with initially poor referring capabilities more than doubles its performance, approaching parity with a model twice its size.