# Question-Aware Gaussian Experts for Audio-Visual Question Answering

arXiv: https://arxiv.org/abs/2503.04459 · HTML: https://mlsanity.com/paper/2503.04459/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 36 of its 47 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)
- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Builds on (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Token Merging: Your ViT But Faster](https://mlsanity.com/paper/2210.09461/) (2022)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Learning to Answer Questions in Dynamic Audio-Visual Scenarios](https://mlsanity.com/paper/2203.14072/) (2022)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [Hierarchical Conditional Relation Networks for Video Question Answering](https://mlsanity.com/paper/2002.10698/) (2020)
- [To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression](https://mlsanity.com/paper/1804.07014/) (2018)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)
- [VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset](https://mlsanity.com/paper/2304.08345/) (2023)
- [Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/1910.14303/) (2019)
- [Can I Trust Your Answer? Visually Grounded Video Question Answering](https://mlsanity.com/paper/2309.01327/) (2023)
- [Relation-Aware Graph Attention Network for Visual Question Answering](https://mlsanity.com/paper/1903.12314/) (2019)
- [Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videos](https://mlsanity.com/paper/2110.05122/) (2021)
- [Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction](https://mlsanity.com/paper/1909.05010/) (2019)
- [MovieChat+: Question-aware Sparse Memory for Long Video Question Answering](https://mlsanity.com/paper/2404.17176/) (2024)
- [Vision Transformers are Parameter-Efficient Audio-Visual Learners](https://mlsanity.com/paper/2212.07983/) (2022)
- [A Simple Baseline for Audio-Visual Scene-Aware Dialog](https://mlsanity.com/paper/1904.05876/) (2019)
- [You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos](https://mlsanity.com/paper/2303.07863/) (2023)
- [Temporal Reasoning via Audio Question Answering](https://mlsanity.com/paper/1911.09655/) (2019)
- [Question Aware Vision Transformer for Multimodal Reasoning](https://mlsanity.com/paper/2402.05472/) (2024)
- [Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks](https://mlsanity.com/paper/2311.05152/) (2023)
- [Progressive Spatio-temporal Perception for Audio-Visual Question Answering](https://mlsanity.com/paper/2308.05421/) (2023)
- [Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering](https://mlsanity.com/paper/2310.06238/) (2023)
- [Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering](https://mlsanity.com/paper/2312.12816/) (2023)
- [Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering](https://mlsanity.com/paper/2404.12020/) (2024)

## Built on by (most cited first, 12 of 12)

- [Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting](https://mlsanity.com/paper/2510.04401/) (2025)
- [AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering](https://mlsanity.com/paper/2510.18346/) (2025)
- [RA-Touch: Retrieval-Augmented Touch Understanding with Enriched Visual Data](https://mlsanity.com/paper/2505.14270/) (2025)
- [MoLT: Mixture of Layer-Wise Tokens for Efficient Audio-Visual Learning](https://mlsanity.com/paper/2512.00115/) (2025)
- [Query-Guided Spatial-Temporal-Frequency Interaction for Music Audio-Visual Question Answering](https://mlsanity.com/paper/2601.19821/) (2026)
- [Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering](https://mlsanity.com/paper/2602.04924/) (2026)
- [Separate First, Fuse Later: Mitigating Cross-Modal Interference in Audio-Visual LLMs Reasoning with Modality-Specific Chain-of-Thought](https://mlsanity.com/paper/2605.09906/) (2026)
- [Crab+: A Scalable and Unified Audio-Visual Scene Understanding Model with Explicit Cooperation](https://mlsanity.com/paper/2603.04128/) (2026)
- [Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs](https://mlsanity.com/paper/2604.14520/) (2026)
- [Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification](https://mlsanity.com/paper/2607.10278/) (2026)
- [Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering](https://mlsanity.com/paper/2511.23304/) (2025)
- [Q-TriM: Question-Guided Tri-Modal Attention for Audio-Visual Question Answering](https://mlsanity.com/paper/2607.03825/) (2026)

## Abstract (arXiv)

Audio-Visual Question Answering (AVQA) requires not only question-based multimodal reasoning but also precise temporal grounding to capture subtle dynamics for accurate prediction. However, existing methods mainly use question information implicitly, limiting focus on question-specific details. Furthermore, most studies rely on uniform frame sampling, which can miss key question-relevant frames. Although recent Top-K frame selection methods aim to address this, their discrete nature still overlooks fine-grained temporal details. This paper proposes QA-TIGER, a novel framework that explicitly incorporates question information and models continuous temporal dynamics. Our key idea is to use Gaussian-based modeling to adaptively focus on both consecutive and non-consecutive frames based on the question, while explicitly injecting question information and applying progressive refinement. We leverage a Mixture of Experts (MoE) to flexibly implement multiple Gaussian models, activating temporal experts specifically tailored to the question. Extensive experiments on multiple AVQA benchmarks show that QA-TIGER consistently achieves state-of-the-art performance. Code is available at https://aim-skku.github.io/QA-TIGER/