Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
The rapid development of multimodal large language models has resulted in
remarkable advancements in visual perception and understanding, consolidating
several tasks into a single visual question-answering framework. However, these
models are prone to hallucinations, which limit their reliability as artificial
intelligence systems. While this issue is extensively researched in natural
language processing and image captioning, there remains a lack of investigation
of hallucinations in Low-level Visual Perception and Understanding (HLPU),
especially in the context of image quality assessment tasks. We consider that
these hallucinations arise from an absence of clear self-awareness within the
models. To address this issue, we first introduce the HLPU instruction
database, the first instruction database specifically focused on hallucinations
in low-level vision tasks. This database contains approximately 200K
question-answer pairs and comprises four subsets, each covering different types
of instructions. Subsequently, we propose the Self-Awareness Failure
Elimination (SAFEQA) model, which utilizes image features, salient region
features and quality features to improve the perception and comprehension
abilities of the model in low-level vision tasks. Furthermore, we propose the
Enhancing Self-Awareness Preference Optimization (ESA-PO) framework to increase
the model's awareness of knowledge boundaries, thereby mitigating the incidence
of hallucination. Finally, we conduct comprehensive experiments on low-level
vision tasks, with the results demonstrating that our proposed method
significantly enhances self-awareness of the model in these tasks and reduces
hallucinations. Notably, our proposed method improves both accuracy and
self-awareness of the proposed model and outperforms close-source models in
terms of various evaluation metrics.
