Thermal x Vision Support about imagebind HOT 5 OPEN

What dataset did you use for your thermal data? Did you use LLVIP in the paper?

from imagebind.

Done?

from imagebind.

Where did you add the function "load_and_transform_thermal_data" exactly? I am facing a different issue though but this might help!, my issue is this:
Given groups=1, weight of size [768, 1, 16, 16], expected input[3, 3, 224, 224] to have 1 channels, but got 3 channels instead

Thanks in advance!

from imagebind.

Also I think there is a typo in line 2, replace image_paths with thermal_paths

from imagebind.

Hi, here to recommend our work, which is LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment. We open source all training and validation code.

LanguageBind can be disassembled into different branches to handle different tasks.
print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Video x Thermal: \n", torch.softmax(embeddings['video'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy())

from imagebind.