JourneyBench

JourneyBench是由哥伦比亚大学、加州大学洛杉矶分校和弗吉尼亚理工大学共同创建的一个综合性视觉语言理解基准。该数据集包含约13500条生成的图像和问题对，旨在测试模型在五个任务中的细粒度多模态推理能力。数据集的内容包括多图像视觉问答、虚构图像描述、视觉问答与幻觉触发器等。创建过程中，采用了人机协作框架生成高质量数据，并通过多轮注释和一致性检查确保数据质量。JourneyBench主要应用于评估和提升多模态大语言模型在复杂和非传统视觉场景中的表现。

JourneyBench is a comprehensive visual-language understanding benchmark co-developed by Columbia University, University of California, Los Angeles (UCLA), and Virginia Tech. This dataset comprises approximately 13,500 curated image-question pairs, built to evaluate models' fine-grained multimodal reasoning abilities across five specific tasks. The dataset encompasses tasks including multi-image visual question answering (VQA), fictional image captioning, visual question answering with hallucination triggers, and more. During its construction, a human-machine collaborative framework was utilized to generate high-quality data, with multi-round annotation and consistency verification procedures implemented to ensure data reliability and quality. JourneyBench is primarily intended for evaluating and enhancing the performance of multimodal large language models (LLMs) in complex and non-traditional visual scenarios.

搜集汇总

数据集介绍

构建方式

JourneyBench is meticulously constructed using a novel human-machine-in-the-loop (HMIL) framework, leveraging prompt-based generated images from platforms like Midjourney. The dataset is curated to include diverse and challenging imaginary images, ensuring a balance between unusual and comprehensible content. Human annotators meticulously filter and categorize these images based on criteria such as unusualness, fictionality, and comprehensibility, ensuring that each image meets the benchmark's rigorous standards. This process involves multiple rounds of annotation and verification to guarantee the dataset's quality and diversity.

特点

JourneyBench stands out for its comprehensive and challenging nature, featuring a wide array of unusual and fictional images that push the boundaries of traditional vision-language understanding benchmarks. The dataset is meticulously annotated with fine-grained categories, ensuring that each task requires sophisticated multimodal reasoning. Additionally, JourneyBench includes sample-specific distractors, making the retrieval tasks particularly challenging by requiring models to differentiate intricate details. This unique combination of diversity, complexity, and adversarial elements makes JourneyBench an exceptional tool for evaluating the limits of current vision-language models.

使用方法

JourneyBench is designed to be a versatile benchmark for evaluating various vision-language understanding tasks. Researchers and practitioners can use the dataset to test models on tasks such as complementary multimodal chain-of-thought, multi-image visual question answering, imaginary image captioning, visual question answering with hallucination triggers, and fine-grained cross-modal retrieval. The dataset's fine-grained annotations and diverse scenarios allow for a thorough assessment of models' abilities to handle unusual and complex visual contexts. By leveraging JourneyBench, users can gain insights into their models' strengths and weaknesses, facilitating further advancements in multimodal AI research.

背景与挑战

背景概述

JourneyBench, introduced in 2024 by researchers from Columbia University, UCLA, and Virginia Tech, is a comprehensive vision-language understanding benchmark designed to assess the fine-grained multimodal reasoning abilities of models. The dataset comprises 13,631 unique image-text samples, leveraging prompt-based generated images to create diverse and challenging scenarios. JourneyBench addresses the limitations of existing benchmarks by focusing on unusual and fictional images, which require models to perform fine-grained multimodal reasoning without relying on shallow visual understanding or language biases.

当前挑战

The primary challenges associated with JourneyBench include the need for fine-grained multimodal reasoning in unusual and fictional scenarios, which existing models often struggle with. The dataset's creation process involved significant challenges in generating high-quality, diverse, and interesting images that bypass copyright issues and offer nuanced testing scenarios. Additionally, the dataset introduces sample-specific distractors to enhance the difficulty of cross-modal retrieval tasks, requiring models to differentiate intricate details. The tasks in JourneyBench, such as complementary multimodal chain-of-thought, multi-image VQA, and imaginary image captioning, are designed to test the limits of models' biases, hallucination tendencies, and fine-grained perception abilities.

常用场景

经典使用场景

JourneyBench 数据集的经典使用场景在于评估多模态大语言模型在视觉语言理解任务中的表现。该数据集通过包含生成图像的复杂视觉场景，测试模型在多模态链式推理、多图像视觉问答、虚构图像描述生成、视觉问答以及跨模态检索等任务中的能力。这些任务要求模型不仅具备浅层的视觉理解，还需进行细粒度的多模态推理，从而有效评估模型在处理不寻常或虚构场景时的表现。

解决学术问题

JourneyBench 数据集解决了当前视觉语言理解基准数据集中普遍存在的图像多样性不足和场景复杂度低的问题。通过引入生成图像，该数据集能够更全面地测试模型在处理不寻常和虚构场景时的表现，从而推动多模态大语言模型在视觉理解能力上的进步。此外，JourneyBench 还通过细粒度的分类和复杂的任务设计，揭示了现有模型在视觉推理和多模态协同处理方面的局限性，为未来的研究提供了明确的方向。

衍生相关工作

JourneyBench 数据集的发布催生了一系列相关研究工作，特别是在多模态大语言模型的评估和改进方面。研究者们利用 JourneyBench 进行模型性能的基准测试，发现了现有模型在处理复杂视觉场景时的不足，并提出了多种改进方法。例如，有研究通过增强模型的视觉特征提取能力来提高其在 JourneyBench 上的表现；还有研究探索了更有效的多模态融合策略，以提升模型在跨模态推理任务中的准确性。这些研究不仅推动了多模态大语言模型的发展，也为实际应用中的模型部署提供了技术支持。