https://github.com/GeWu-Lab/MUSIC-AVQA
공식 코드Learning to Answer Questions in Dynamic Audio-Visual Scenarios
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Learning to Answer Questions in Dynamic Audio-Visual Scenarios
Can audio-visual integration strengthen robustness under multimodal attacks?
Boosting Audio-visual Zero-shot Learning with Large Language Models
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers
Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser
AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Deep Video Inpainting Guided by Audio-Visual Self-Supervision
AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Class-Incremental Grouping Network for Continual Audio-Visual Learning
A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition
Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
Enhancing Sound Source Localization via False Negative Elimination
AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.