Konsep Inti
M2UGen introduces a framework for multi-modal music understanding and generation using large language models.
Statistik
"The MU-LLaMA model [47] stands as a representative, which is trained on a dedicated music question-answering dataset."
"The ViViT model produces embeddings with a shape of (3137, 768), where 3137 is derived from the total count of 16×16 patches sampled uniformly from 32 frames of size 224 × 224, including the final output layer, and 768 is the hidden size of the Transformer."
"The M2UGen model performs better when given AudioLDM 2 or MusicGen as the music decoder compared to using them alone."
Kutipan
"The M2UGen model outperforms or achieves SOTA performance in various tasks, including music understanding, music editing, and text/image/video-to-music generation."
"Our future work will focus on further enhancing the model’s fine-grained music understanding capabilities."