“视觉语言常识推理是一类旨在理解视觉场景的任务,常用于评估人工智能系统的多模态常识推理能力。然而,可靠的常识推理需要细致的场景理解,而现有的基于预训练模型微调的方法却无法有效地利用具体场景中存在的物体关系信息,因此其推理的合理性存在较大的局限性。为解决上述问题,本研究提出了一种场景图增强的视觉语言常识推理生成框架SGEVL。该框架首先使用图像补丁序列提供视觉信息,并通过一种包含注意力模块的门控机制,赋予大型语言模型理解视觉信息的能力。基于该框架的视觉语言能力,进一步提出了一种无位置信息的场景图生成方法。生成的场景图能够显著提升模型对场景信息的理解,从而引导生成高质量的回答和推理。通过在VCR,VQA-X和e-SNLI-VE数据集上分别实验,实验结果表明本文提出的视觉语言常识推理框架性能优于基线模型。此外,通过消融实验和结果可视化,进一步证明了该框架中每个模块的有效性。”

Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. However, these models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. Many existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. To address this issue, we propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD). This framework incorporates hallucination feedback at both object and sentence semantic levels. Remarkably, even with a marginal degree of training, this approach can alleviate over 15% of hallucination. Simultaneously, HELPD penalizes the output logits according to the image attention window to avoid being overly affected by generated text. HELPD can be seamlessly integrated with any LVLMs. Our experiments demonstrate that the proposed framework yields favorable results across multiple hallucination benchmarks. It effectively mitigates hallucination for different LVLMs and concurrently improves their text generation quality.