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New SHRIKE model advances audio-visual question answering with scene graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SHRIKE, a novel system for audio-visual question answering that utilizes a multi-modal scene graph and a Kolmogorov-Arnold Network (KAN)-based Mixture of Experts (MoE). This approach explicitly models objects and their relationships within audio-visual scenes, addressing limitations in existing methods that struggle with structural video information and fine-grained multi-modal feature modeling. SHRIKE achieves state-of-the-art performance on the MUSIC-AVQA and MUSIC-AVQA v2 benchmarks, demonstrating improved temporal reasoning and cross-modal interaction capabilities. AI

IMPACT Advances audio-visual reasoning capabilities, potentially improving AI's understanding of complex scenes and interactions.

RANK_REASON The cluster describes a new research paper detailing a novel model and its performance on established benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SHRIKE model advances audio-visual question answering with scene graphs

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The cluster describes a new research paper detailing a novel model and its performance on established benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zijian Fu, Changsheng Lv, Xianlin Zhang, Mengshi Qi, Huadong Ma ·

    Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering

    arXiv:2511.23304v2 Announce Type: replace Abstract: In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE). The task aims to mimic human reasoning by extracting and fusing information from audio…

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