Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos
Authors: name: Sreyan Ghosh, name: Arushi Goel, name: Kaousheik Jayakumar, name: Lasha Koroshinadze, name: Nishit Anand, name: Siddharth Gururani, name: Hanrong Ye, name: Pritam Biswas, name: Yuanhang Su, name: Ehsan Hosseini-Asl, name: Sang-gil Lee, name: Zhifeng Kong, name: Jaehyeon Kim, name: Sungwon Kim, name: S Sakshi, name: Ramani Duraiswami, name: Dinesh Manocha, name: Andrew Tao, name: Mohammad Shoeybi, name: Bryan Catanzaro, name: Ming-Yu Liu, name: Wei Ping
Organization: NVIDIA

We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large language model (AV-LLM) for joint understanding and reasoning over audio, images, and long-form videos. Unlike prior AV-LLMs that primarily focus on short clips, AV-Flamingo is designed for understanding and reasoning over long and complex real-world (audio-visual) videos. To support this, we make three key contributions: (i) Audio-Visual-Skills, a large-scale collection of real-world videos with ~7M caption and question-answer training instances designed to emphasize temporal, compositional, and cross-modal audio-visual reasoning; (ii) a novel three-stage curriculum that progressively trains the model from short-range perception to long-horizon multi-event reasoning; and (iii) Temporal Audio-Visual Interleaved Chain-of-Thought, a reasoning framework that explicitly grounds intermediate reasoning steps to timestamps in long audio-visual streams, improving temporal alignment and interpretability. Extensive experiments across 15+ audio-visual, omni-modal, audio, and vision benchmarks show that AV-Flamingo outperforms similarly sized open models by clear margins and remains highly competitive with, and in some cases surpasses, much larger open-weight and closed models, particularly on long and complex real-world audio-visual understanding and reasoning tasks. Beyond benchmark performance, AV-Flamingo exhibits strong real-world utility and transfers well to unseen tasks, highlighting its robustness and generalization ability.