Efficient Audio-Visual Fusion for Video Classification
This work addresses the problem of efficient multimodal fusion for video classification, but it is incremental as it builds on existing methods with a focus on efficiency.
The authors tackled the challenge of efficiently fusing audio and visual modalities for video classification, achieving competitive performance on the YouTube-8M dataset with reduced model complexity.
We present Attend-Fusion, a novel and efficient approach for audio-visual fusion in video classification tasks. Our method addresses the challenge of exploiting both audio and visual modalities while maintaining a compact model architecture. Through extensive experiments on the YouTube-8M dataset, we demonstrate that our Attend-Fusion achieves competitive performance with significantly reduced model complexity compared to larger baseline models.