on audio from Audioset and (2) a supervised AudioMAE
model finetuned on audio classification. Both baselines
use the same capacity ViT-B audio encoder as IMAGE-
BIND.
IMAGEBIND significantly outperforms the Au-
dioMAE model on all settings with gains of ∼40% accuracy
in top-1 accuracy on ≤4-shot classification. IMAGEBIND
also matches or outperforms the supervised model on ≥1-
shot classification. IMAGEBIND’s emergent zero-shot per-
formance surpasses the supervised ≤2-shot performance.
For few-shot depth classification, we compare with the
multimodal MultiMAE [4] ViT-B/16 model trained on im-
ages, depth, and semantic segmentation data. IMAGEBIND
significantly outperforms MultiMAE across all the few-shot
settings. Altogether, these results show the strong gener-
alization of IMAGEBIND audio and depth features trained
with image alignment.
IMAGEBIND- One Embedding Space To Bind Them A