JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation

• URL: http://arxiv.org/abs/2512.12772v1
• Date: Sun, 14 Dec 2025 17:23:21 GMT
• Title: JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation
• Authors: Jianghan Chao, Jianzhang Gao, Wenhui Tan, Yuchong Sun, Ruihua Song, Liyun Ru,
• Abstract summary: We evaluate leading vision-only, audio-only, and Omni-LLMs on our dataset.<n>Even the best-performing Omni-LLM achieves an average accuracy of only 62.6%, outperforming uni-modal baselines.
• Score: 16.067014259345743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding videos inherently requires reasoning over both visual and auditory information. To properly evaluate Omni-Large Language Models (Omni-LLMs), which are capable of processing multi-modal information including vision and audio, an effective benchmark must comprehensively cover three key aspects: (1) multi-modal dependency (i.e., questions that cannot be answered using vision or audio alone), (2) diverse audio information types (e.g., speech, sound events), and (3) varying scene spans. However, existing datasets fall short in one or more of these dimensions, limiting strict and comprehensive evaluation. To address this gap, we introduce JointAVBench, a novel benchmark with strict audio-video correlation, spanning five cognitive dimensions, four audio information types (speech, sound events, music, vocal traits), and three scene spans (single-, cross-, and full-scene). Given the high cost of manual annotation, we propose an automated pipeline that leverages state-of-the-art vision-LLMs, audio-LLMs, and general-purpose LLMs to synthesize questions and answers that strictly require joint audio-visual understanding. We evaluate leading vision-only, audio-only, and Omni-LLMs on our dataset. Results show that even the best-performing Omni-LLM achieves an average accuracy of only 62.6\%, outperforming uni-modal baselines but revealing substantial room for improvement, especially in cross-scene reasoning.

Related papers

• Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception [97.32606786622728]
  We present a systematic and comprehensive investigation of omni detailed perception from the perspectives of the data pipeline, models, and benchmark.<n>We propose Omni-Detective, an agentic data generation pipeline integrating tool-calling, to autonomously produce highly detailed yet minimally hallucinatory multimodal data.<n>Based on the data generated with Omni-Detective, we train two captioning models: Audio-Captioner for audio-only detailed perception, and Omni-Captioner for audio-visual detailed perception.
  arXiv  Detail & Related papers  (2025-10-14T17:00:09Z)
• AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
  multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
  arXiv  Detail & Related papers  (2025-08-29T07:40:39Z)
• Aligned Better, Listen Better for Audio-Visual Large Language Models [21.525317311280205]
  Video inherently contains audio, which supplies information to vision.<n>Video large language models (Video-LLMs) can encounter many audio-centric settings.<n>Existing models exhibit deficiencies in exploiting audio information, leading to weak understanding and hallucinations.
  arXiv  Detail & Related papers  (2025-04-02T18:47:09Z)
• Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision [83.0622534215881]
  This work proposes an industry-level omni-modal large language model (LLM) pipeline that integrates auditory, visual, and linguistic modalities.<n>Our pipeline consists of three main components: First, a modular framework enabling flexible configuration of various encoder-LLM-decoder architectures.<n>Second, a lightweight training strategy that pre-trains audio-language alignment on the state-of-the-art vision-language model Qwen2.5-VL.<n>Third, an audio synthesis pipeline that generates high-quality audio-text data from diverse real-world scenarios.
  arXiv  Detail & Related papers  (2025-02-26T17:26:36Z)
• AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information? [65.49972312524724]
  multimodal large language models (MLLMs) have expanded their capabilities to include vision and audio modalities.<n>Our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans find trivial.<n>We introduce AV-Odyssey Bench, a comprehensive audio-visual benchmark designed to assess whether those MLLMs can truly understand the audio-visual information.
  arXiv  Detail & Related papers  (2024-12-03T17:41:23Z)
• Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models [25.660343393359565]
  This paper proposes a fine-grained audio-visual joint representation (FAVOR) learning framework for multimodal large language models (LLM) FAVOR simultaneously perceive speech and audio events in the audio input stream and images or videos in the visual input stream, at the frame level. An interactive demo of FAVOR is available at https://github.com/BriansIDP/AudioVisualLLM.git, and the training code and model checkpoints will be released soon.
  arXiv  Detail & Related papers  (2023-10-09T17:00:20Z)
• MAViL: Masked Audio-Video Learners [68.61844803682145]
  We present Masked Audio-Video learners (MAViL) to train audio-visual representations. Pre-training with MAViL enables the model to perform well in audio-visual classification and retrieval tasks. For the first time, a self-supervised audio-visual model outperforms ones that use external supervision on benchmarks.
  arXiv  Detail & Related papers  (2022-12-15T18:59:59Z)
• AudioVisual Video Summarization [103.47766795086206]
  In video summarization, existing approaches just exploit the visual information while neglecting the audio information. We propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this.
  arXiv  Detail & Related papers  (2021-05-17T08:36:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.