AV-Odyssey 實驗平台:您的多模態語言模型真的能理解視聽資訊嗎?

AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?

December 3, 2024
作者: Kaixiong Gong, Kaituo Feng, Bohao Li, Yibing Wang, Mofan Cheng, Shijia Yang, Jiaming Han, Benyou Wang, Yutong Bai, Zhuoran Yang, Xiangyu Yue
cs.AI

摘要

最近,多模式大型語言模型(MLLMs),如GPT-4o、Gemini 1.5 Pro和Reka Core,已擴展其功能以包括視覺和音訊模式。儘管這些模型在各種視聽應用中展現出令人印象深刻的性能,但我們提出的DeafTest顯示,MLLMs常常在人類認為微不足道的簡單任務上遇到困難:1)確定兩個聲音中哪個更大聲,以及2)確定兩個聲音中哪個音調更高。受到這些觀察的啟發,我們引入了AV-Odyssey Bench,這是一個全面的音視覺基準,旨在評估這些MLLMs是否真正理解音視覺信息。該基準包含了4555個精心設計的問題,每個問題都包含文本、視覺和音訊元素。為了成功推斷答案,模型必須有效地利用來自視覺和音訊輸入的線索。為確保對MLLM回答的精確和客觀評估,我們將問題設計為多選項,消除了對人類評估或LLM輔助評估的需求。我們對一系列封閉源和開源模型進行基準測試並總結觀察結果。通過揭示當前模型的限制,我們旨在為未來數據集收集和模型開發提供有用的見解。
English
Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans find trivial: 1) determining which of two sounds is louder, and 2) determining which of two sounds has a higher pitch. Motivated by these observations, we introduce AV-Odyssey Bench, a comprehensive audio-visual benchmark designed to assess whether those MLLMs can truly understand the audio-visual information. This benchmark encompasses 4,555 carefully crafted problems, each incorporating text, visual, and audio components. To successfully infer answers, models must effectively leverage clues from both visual and audio inputs. To ensure precise and objective evaluation of MLLM responses, we have structured the questions as multiple-choice, eliminating the need for human evaluation or LLM-assisted assessment. We benchmark a series of closed-source and open-source models and summarize the observations. By revealing the limitations of current models, we aim to provide useful insight for future dataset collection and model development.

Summary

AI-Generated Summary

Paper Overview

This literature evaluates Multimodal Large Language Models (MLLMs) on audio-visual tasks using the AV-Odyssey Bench. It identifies limitations in current models' audio-visual comprehension and integration, emphasizing the need for advancements in this area.

Core Contribution

The study introduces the AV-Odyssey Bench, a comprehensive audio-visual benchmark challenging MLLMs with 4,555 problems across 26 tasks, highlighting deficiencies in audio-visual integration.

Research Context

The research addresses the gap in understanding MLLMs' audio-visual capabilities, focusing on tasks like pitch comparison, sound duration recognition, and audio-visual information integration.

Keywords

Multimodal Large Language Models (MLLMs), AV-Odyssey Bench, Audio-Visual Integration, Benchmark Evaluation, Audio Understanding, Vision Understanding

Background

This study aims to evaluate MLLMs' performance on audio-visual tasks, highlighting challenges in discriminating sound volume, pitch, and integrating audio-visual information effectively.

Research Gap

Existing literature lacks in-depth evaluation of MLLMs' audio-visual comprehension, especially in discriminating sound attributes and processing complex audio-visual tasks.

Technical Challenges

MLLMs face difficulties in discriminating sound volume, higher pitch, and integrating audio and visual modalities effectively for accurate inference.

Prior Approaches

Previous benchmarks did not adequately test MLLMs' audio-visual integration capabilities, lacking complexity and domain focus compared to the AV-Odyssey Bench.

Methodology

The study employs the AV-Odyssey Bench to evaluate MLLMs across various audio-visual tasks, presenting detailed data distribution, evaluation results, and model performance metrics.

Theoretical Foundation

The AV-Odyssey Bench comprises 26 tasks challenging MLLMs in audio-visual integration, requiring models to process audio and visual cues effectively for accurate responses.

Technical Architecture

Models like Unified-IO, VideoLLaMA, Gemini, and GPT-4o are tested on the AV-Odyssey Bench, highlighting strengths and weaknesses in audio-visual comprehension.

Implementation Details

Data curation involves unbiased collection of audio and visual data, while quality control filters ensure fair evaluation. Models are tested in a zero-shot setting without finetuning.

Innovation Points

The AV-Odyssey Bench introduces a novel evaluation method for MLLMs, emphasizing audio-visual integration and providing insights into model limitations.

Experimental Validation

The study experimentally validates MLLMs' performance on the AV-Odyssey Bench, showcasing challenges in audio-visual integration and identifying errors in audio understanding and reasoning.

Setup

Models like Gemini, Reka, Unified-IO, and GPT-4o are tested on the AV-Odyssey Bench, revealing limitations in audio-visual comprehension and integration.

Metrics

Performance metrics like accuracy are reported for tasks involving instrument recognition, singer recognition, gunshot recognition, and other audio-visual challenges.

Results

Models exhibit varying performance on different tasks, with the top-performing model achieving 34.5% accuracy, highlighting the difficulty of the benchmark.

Comparative Analysis

Comparison between open-source and closed-source models reveals similar performance levels, indicating the universal challenge in audio-visual integration for MLLMs.

Impact and Implications

The study's findings underscore the limitations in current MLLMs' audio-visual understanding, emphasizing the need for improved audio-visual integration and model development.

Key Findings

The AV-Odyssey Bench exposes deficiencies in MLLMs' audio-visual comprehension, indicating the necessity for advancements in true audio-visual integration.

Limitations

Current MLLMs struggle with audio understanding errors and reasoning challenges, hindering accurate audio-visual inference and integration.

Future Directions

Future research should focus on enhancing multi-modal reasoning, improving audio-visual integration, and developing datasets that challenge MLLMs' audio-visual capabilities.

Practical Significance

Advancements in audio-visual integration can lead to more human-like audio-visual understanding in MLLMs, benefiting various applications requiring multi-modal comprehension.

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