Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities (2505.17862v1)

Abstract: Recent Multimodal LLMs (MLLMs) achieve promising performance on visual and audio benchmarks independently. However, the ability of these models to process cross-modal information synchronously remains largely unexplored. In this paper, we introduce: 1) Daily-Omni, an Audio-Visual Questioning and Answering benchmark comprising 684 videos of daily life scenarios from diverse sources, rich in both audio and visual information, and featuring 1197 multiple-choice QA pairs across 6 major tasks; 2) Daily-Omni QA Generation Pipeline, which includes automatic annotation, QA generation and QA optimization, significantly improves efficiency for human evaluation and scalability of the benchmark; 3) Daily-Omni-Agent, a training-free agent utilizing open-source Visual LLM (VLM), Audio LLM (ALM) and Automatic Speech Recognition (ASR) model to establish a baseline for this benchmark. The results show that current MLLMs still struggle significantly with tasks requiring audio-visual integration, but combining VLMs and ALMs with simple temporal alignment techniques can achieve substantially better performance. Codes and benchmark are available at \href{https://github.com/Lliar-liar/Daily-Omni}{https://github.com/Lliar-liar/Daily-Omni}.

Audio-Visual Reasoning in Multimodal LLMs: The Daily-Omni Benchmark Analysis

The paper "Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities" expounds upon the development and implications of the Daily-Omni benchmark, a sophisticated evaluation framework devised to assess Multimodal LLMs (MLLMs) on audio-visual perception and reasoning tasks. The research introduces novel methodologies and datasets, aiming to address the current limitations in synchronizing cross-modal information processing within MLLMs.

Recent advancements have led to promising performance by MLLMs on separate audio and visual benchmarks; however, synchronized cross-modal information processing remains largely unexplored. The paper introduces three pivotal components: the Daily-Omni benchmark, the Daily-Omni QA Generation Pipeline, and the Daily-Omni Agent.

Key Contributions

1. Daily-Omni Benchmark: This benchmark consists of 684 videos encompassing diverse daily life scenarios, featuring rich audio-visual information. It integrates multiple-choice question-answering pairs across six major tasks, including audio-visual event alignment and cross-modal reasoning, thereby broadening the evaluation scope of MLLMs beyond traditional benchmarks.
2. QA Generation Pipeline: The paper outlines an innovative pipeline for automatic annotation, QA generation, and optimization, significantly improving efficiency and scalability. A meticulous filtering process ensures high-quality QA pairs, facilitating reliable comparisons of model performance.
3. Daily-Omni Agent: This training-free agent combines open-source VLM, ALM, and ASR models to establish a baseline for the benchmark. Results underscore the substantial challenges current MLLMs face in tasks demanding audio-visual integration.

Numerical Results and Claims

The empirical evaluation underscores that many existing MLLMs struggle with audio-visual tasks, particularly those requiring temporal alignment and integration across modalities. The Daily-Omni Agent achieved state-of-the-art performance among open-source methods, signaling the potential of combining simple temporal alignment techniques with existing models to enhance multimodal reasoning capabilities.

The paper provides compelling evidence that synchronized audio-visual processing remains an unsolved challenge for MLLMs. Consequently, substantial constraints are evident in their ability to comprehend rich acoustic environments and integrate temporally aligned multimodal data.

Implications for AI Research and Development

The introduction of Daily-Omni is a vital step toward evolving MLLMs capable of sophisticated real-world audio-visual reasoning. By providing a systematic framework for evaluating and improving cross-modal processing capabilities, the benchmark addresses a critical gap in the landscape of multimodal datasets, characterized by their specialization and lack of temporal alignment.

This research lays the groundwork for future investigations into multimodal temporal grounding techniques and comprehensive multimodal integration strategies. As MLLMs become increasingly pivotal in real-world applications, this benchmark highlights the necessity for more advanced architectures equipped to process synchronized cross-modal information efficiently.

Future Directions

Future developments may involve the refinement of alignment techniques, fostering audio-visual models adept at complex multimodal reasoning. Enhanced architectural designs and richer datasets will be essential to overcome current limitations, ensuring MLLMs can fully leverage their audio-visual capabilities in practical domains. Moreover, exploring scalable methodologies for dataset augmentation will be crucial in supporting systematic expansion and broad applicability of the Daily-Omni benchmark.

In conclusion, this paper delineates a significant initiative in advancing audio-visual reasoning within MLLMs, presenting a structured evaluation benchmark that underscores the intricacies of temporal alignment across modalities. It sets a precedent for ongoing research aimed at realizing truly sophisticated multimodal LLMs capable of deep integration and understanding of audio-visual information in dynamic real-world contexts.