CLIP to extract semantically strong video representations
[14, 42, 44, 77]. Most related to our method, Nagrani et
al. [50] create a weakly-labeled dataset for paired video-
audio and captions that allows for training multi-modal
video-audio encoder to match textual features resulting in
strong audio and video retrieval and captioning perfor-
mance. AudioCLIP [26] adds audio as an additional modal-
ity into a CLIP framework, enabling zero-shot audio classi-
fication. In contrast, IMAGEBIND does not require explicit
paired data between all modalities and instead leverages im-
age as a natural weak supervision for unifying modalities.
Feature Alignment Pre-trained CLIP models have been
utilized as teachers to supervise other models due to the
strength of its visual representations [43, 57, 73]. More-
over, CLIP joint image and text embedding space has also
been leveraged for a variety of zero-shot tasks like de-
tection [23, 86], segmentation [40], mesh animation [79]
IMAGEBIND- One Embedding Space To Bind Them A