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Audio-visual Question Answering
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Music's Multimodal Complexity in AVQA: Why We Need More than General Multimodal LLMs
FortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning
PAVE: Patching and Adapting Video Large Language Models
Question-Aware Gaussian Experts for Audio-Visual Question Answering
Patch-level Sounding Object Tracking for Audio-Visual Question Answering
SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering
OMCAT: Omni Context Aware Transformer
Boosting Audio Visual Question Answering via Key Semantic-Aware Cues
Learning Trimodal Relation for AVQA with Missing Modality
Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time
SHMamba: Structured Hyperbolic State Space Model for Audio-Visual Question Answering
Towards Multilingual Audio-Visual Question Answering
CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering
Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering
Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering
CAD -- Contextual Multi-modal Alignment for Dynamic AVQA
Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering
Progressive Spatio-temporal Perception for Audio-Visual Question Answering
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
Vision Transformers are Parameter-Efficient Audio-Visual Learners
Learning to Answer Questions in Dynamic Audio-Visual Scenarios
Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.