Supervision-free Vision-Language Alignment

• URL: http://arxiv.org/abs/2501.04568v1
• Date: Wed, 08 Jan 2025 15:32:12 GMT
• Title: Supervision-free Vision-Language Alignment
• Authors: Giorgio Giannone, Ruoteng Li, Qianli Feng, Evgeny Perevodchikov, Rui Chen, Aleix Martinez,
• Abstract summary: We introduce SVP (Supervision-free Visual Projection), a framework that enhances vision-language alignment without relying on curated data or preference annotation. We evaluate our approach across six key areas: captioning, referring, visual question answering, multitasking, hallucination control, and object recall.
• Score: 11.012355590697064
• License:
• 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 (Supervision-free Visual Projection), a novel framework that enhances vision-language alignment without relying on curated data or preference annotation. SVP leverages 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 substantial reduction in hallucination rates. Notably, a small VLM using SVP achieves hallucination reductions comparable 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.

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