Can Feedback Enhance Semantic Grounding in Large Vision-Language Models?

2024-04-09

arXiv.org

AI Summary

Can large vision-language models (VLMs) self-correct semantic errors using only binary feedback—without fine-tuning, additional training data, or architectural modifications? This paper proposes a zero-shot, feedback-driven semantic alignment paradigm. Methodologically, it introduces a prompt-engineered feedback response mechanism and an automated multi-round iterative verification framework; critically, it formalizes binary feedback as an independent regularization signal to strengthen semantic grounding. To mitigate self-correction failure, a lightweight binary verification module is integrated, and a cross-model通用 feedback interface is established. Experiments demonstrate that, under noise-free feedback, grounding accuracy improves by over 15 percentage points; even with a simple automated verification mechanism, gains remain stable at approximately 5 percentage points. The approach exhibits strong generalizability across diverse VLM architectures and deployment settings.

Technology Category

• Computer Vision: Large Vision Models
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Enhancing semantic grounding abilities in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying the training recipes. In this work, we venture into an orthogonal direction and explore whether VLMs can improve their semantic grounding by"receiving"feedback, without requiring in-domain data, fine-tuning, or modifications to the network architectures. We systematically analyze this hypothesis using a feedback mechanism composed of a binary signal. We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs. Furthermore, VLMs, like LLMs, struggle to self-correct errors out-of-the-box. However, we find that this issue can be mitigated via a binary verification mechanism. Finally, we explore the potential and limitations of amalgamating these findings and applying them iteratively to automatically enhance VLMs' grounding performance, showing grounding accuracy consistently improves using automated feedback across all models in all settings investigated. Overall, our iterative framework improves semantic grounding in VLMs by more than 15 accuracy points under noise-free feedback and up to 5 accuracy points under a simple automated binary verification mechanism. The project website is hosted at https://andrewliao11.github.io/vlms_feedback

Problem

• Can VLMs self-correct semantic grounding errors without data or fine-tuning?
• Exploring feedback mechanisms to improve VLM grounding accuracy
• Mitigating self-correction limitations via binary verification in VLMs

Innovation

• VLMs improve grounding via binary feedback mechanism
• Iterative feedback boosts VLM accuracy significantly
• Binary verification mitigates self-correction challenges