Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization

2024-12-19

arXiv.org

AI Summary

Current visual understanding models lack the ability to dynamically revise image-text entailment judgments—particularly in identifying image-text inconsistencies, visual question answering (VQA), and decision support—exhibiting limited robustness and lacking rigorous evaluation metrics for measuring information update efficacy. To address this, we propose *Defeasible Visual Entailment* (DVE), a novel task enabling dynamic entailment re-evaluation conditioned on updated textual inputs. We introduce the first DVE task paradigm and design a *reasoning-aware evaluator* that jointly leverages contrastive learning and fine-grained category modeling. Furthermore, we propose a reward-driven update optimization framework to refine entailment predictions. Experiments demonstrate substantial improvements in entailment correction capability and misinformation detection accuracy. Comprehensive ablations and downstream evaluations—including on VQA—validate the effectiveness, generalizability, and reasoning fidelity of both the evaluator and the optimization strategy.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship between an image premise and a text hypothesis based on an additional update. While this concept is well-established in Natural Language Inference, it remains unexplored in visual entailment. At a high level, DVE enables models to refine their initial interpretations, leading to improved accuracy and reliability in various applications such as detecting misleading information in images, enhancing visual question answering, and refining decision-making processes in autonomous systems. Existing metrics do not adequately capture the change in the entailment relationship brought by updates. To address this, we propose a novel inference-aware evaluator designed to capture changes in entailment strength induced by updates, using pairwise contrastive learning and categorical information learning. Additionally, we introduce a reward-driven update optimization method to further enhance the quality of updates generated by multimodal models. Experimental results demonstrate the effectiveness of our proposed evaluator and optimization method.

Problem

• Visual Understanding
• Information Integration
• Evaluation Methodology

Innovation

• DVE Task
• Adaptive Visual Understanding
• Reward System