video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model

TL;DR

This paper introduces video-SALMONN-o1, a reasoning-enhanced audio-visual large language model designed for general video understanding, featuring a new dataset, a novel optimization method, and a new benchmark, achieving significant performance improvements.

Contribution

The paper presents the first open-source reasoning-enhanced audio-visual LLM for general video understanding, with a new reasoning dataset, process direct preference optimization, and a reasoning-intensive benchmark.

Findings

Achieves 3-8% accuracy improvements over baseline.

pDPO improves performance by 6-8%.

Enables zero-shot synthetic video detection.

Abstract

While recent advancements in reasoning optimization have significantly enhanced the capabilities of large language models (LLMs), existing efforts to improve reasoning have been limited to solving mathematical problems and focusing on visual graphical inputs, neglecting broader applications in general video understanding.This paper proposes video-SALMONN-o1, the first open-source reasoning-enhanced audio-visual LLM designed for general video understanding tasks. To enhance its reasoning abilities, we develop a reasoning-intensive dataset featuring challenging audio-visual questions with step-by-step solutions. We also propose process direct preference optimization (pDPO), which leverages contrastive step selection to achieve efficient step-level reward modelling tailored for multimodal inputs. Additionally, we introduce RivaBench, the first reasoning-intensive video understanding benchmark, featuring over 4,000 high-quality, expert-curated question-answer pairs across scenarios such as standup comedy, academic presentations, and synthetic video detection. video-SALMONN-o1 achieves 3-8% accuracy improvements over the LLaVA-OneVision baseline across different video reasoning benchmarks. Besides, pDPO achieves 6-8% improvements compared to the supervised fine-tuning model on RivaBench. Enhanced reasoning enables video-SALMONN-o1 zero-shot synthetic video detection capabilities.