← 研踪 PaperTrailCVF · CVPR 2026:cvf:21344edce08d352e1de8c370ea176b4d · 记录 #1
EgoAVU: Egocentric Audio-Visual Understanding
Seth, AshishMei, XinhaoZhao, ChangshengNagaraja, VarunChang, ErnieMeyer, Gregory P.Le Lan, GaelXiong, YunyangChandra, VikasShi, YangyangManocha, DineshCai, Zhipeng 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:30
摘要
Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due to the challenge of obtaining text labels with coherent joint-modality information, whether MLLMs can jointly understand both modalities in egocentric videos remains under-explored. To address this problem, we introduce EgoAVU, a scalable data engine to automatically generate egocentric audio-visual narrations, questions and answers. EgoAVU enriches human narrations with multimodal context and generates audio-visual narrations through cross-modal correlation modeling. Token-based video filtering and modular, graph based curation ensure both the data diversity and quality. Leveraging EgoAVU, we construct EgoAVU-Instruct - a large scale training dataset of 3M samples, and EgoAVU-Bench - a manually verified evaluation split of 3K samples. EgoAVU-Bench clearly reveals the limitation of existing MLLMs: they bias heavily towards visual signals, often neglecting audio cues or failing to correspond audio with the visual source. Finetuning MLLMs on EgoAVU-Instruct effectively solves this issue, enabling up to 113% performance improvement on EgoAVU-Bench. Such benefit can also transfer to other benchmarks such as EgoTempo and EgoIllusion, achieving up to 28% relative performance gain. Project page: https://cs20s030.github.io/EgoAVU/
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记录 #1 · 2026 · 最新观测版本
EgoAVU: Egocentric Audio-Visual Understanding
Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due to the challenge of obtaining text labels with coherent joint-modality information, whether MLLMs can jointly understand both modalities in egocentric videos remains under-explored. To address this problem, we introduce EgoAVU, a scalable data engine to automatically generate egocentric audio-visual narrations, questions and answers. EgoAVU enriches human narrations with multimodal context and generates audio-visual narrations through cross-modal correlation modeling. Token-based video filtering and modular, graph based curation ensure both the data diversity and quality. Leveraging EgoAVU, we construct EgoAVU-Instruct - a large scale training dataset of 3M samples, and EgoAVU-Bench - a manually verified evaluation split of 3K samples. EgoAVU-Bench clearly reveals the limitation of existing MLLMs: they bias heavily towards visual signals, often neglecting audio cues or failing to correspond audio with the visual source. Finetuning MLLMs on EgoAVU-Instruct effectively solves this issue, enabling up to 113% performance improvement on EgoAVU-Bench. Such benefit can also transfer to other benchmarks such as EgoTempo and EgoIllusion, achieving up to 28% relative performance gain. Project page: https://cs20s030.github.io/EgoAVU/
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