Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
This addresses the problem of data curation costs for researchers and practitioners in vision-language AI, offering a novel method that is incremental in reducing reliance on manual annotation.
The paper tackles the challenge of reducing the need for extensive, high-quality image-text training data in vision-language models by introducing SVP, a framework that enhances alignment with minimal human supervision, resulting in improvements such as a 14% average gain in captioning tasks and up to 12% increase in object recall.
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.