https://github.com/AIM-SKKU/QA-TIGER
공식 코드Question-Aware Gaussian Experts for Audio-Visual Question Answering
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Question-Aware Gaussian Experts for Audio-Visual Question Answering
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios
Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering
Learning to Answer Questions in Dynamic Audio-Visual Scenarios
Vision Transformers are Parameter-Efficient Audio-Visual Learners
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
Progressive Spatio-temporal Perception for Audio-Visual Question Answering
Boosting Audio Visual Question Answering via Key Semantic-Aware Cues
Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos
FortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning
Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.