From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation

Jiaxin Ge, Sanjay Subramanian, Trevor Darrell, Boyi Li

Submission Type: Regular Long Paper

Submission Track: Language Grounding to Vision, Robotics and Beyond

Keywords: Vision-Language Models, Visual Reasoning, Vision-Language Explanation, Self Training

Abstract: Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a Recursive Visual Explanation algorithm. Our method iteratively computes visual features (conditioned on the text input), an answer, and an explanation, to improve the explanation quality step by step until the answer converges. We find that this multi-step approach guides the model to correct its own answers and outperforms single-step explanation generation. Furthermore, explanations generated by ReVisE also serve as valuable annotations for few-shot self-training. Our approach outperforms previous methods while utilizing merely 5\% of the human-annotated explanations across 10 metrics, demonstrating up to a 4.2 and 1.3 increase in BLEU-1 score on the VCR and VQA-X datasets, underscoring the efficacy and data-efficiency of our method.

Submission Number: 30