Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Year: 2024
Type: preprint
Abstract: Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgment, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM's performance and outperforms previous approaches, achieving superior modality alignment.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1523888516, G4720003262, FAI: Toward Fair Decision Making and Resource Allocation with Application to AI-Assisted Graduate Admission and Degree Completion, G6098521345, G7437616762, TRUSTWORTHY REAL-TIME COMMUNICATIVE MULTI-AGENT REINFORCEMENT LEARNING FOR DECISION-MAKING IN UNSTABLE & ADVERSARIAL SYSTEMS, AUTONOMOUS DECISION-MAKING SYSTEMS UNDER (ADVERSARIAL) DISTRIBUTION SHIFTS: LEARNING TO ADAPT, GENERALIZE AND ROBUSTIFY, G8876996369
OpenAlex ID: W4399115209