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jpthu17 avatar jpthu17 commented on August 10, 2024

I am sorry to reply to you so late because I have been busy with other projects recently.

The relevant code is below, we are currently trying to improve this part of the code and will update our code base in the near future.

cls_features = torch.mean(image_features, dim=1, keepdim=False).unsqueeze(0).clone()
token_dict = {'x': cls_features,
'token_num': cls_features.size(1),
'idx_token': torch.arange(cls_features.size(1))[None, :].repeat(
cls_features.size(0), 1),
'agg_weight': cls_features.new_ones(cls_features.size(0), cls_features.size(1),
1),
'mask': None}
down_dict, token_dict = self.get_model().ctm3(token_dict)
events = OrderedDict()
max_len = 0
for id, i in enumerate(down_dict["idx_token"][0].tolist()):
if i not in events:
events[i] = [id]
else:
events[i].append(id)
max_len = len(events[i]) if max_len < len(events[i]) else max_len

from chat-univi.

xiaokj37 avatar xiaokj37 commented on August 10, 2024

Thanks for your reply.
I am now trying to reproduce the results of the video event slicing given in the paper. You have given the visualization code for the image, is it possible to give the processing code for the video and the corresponding reference video.
I'm waiting for your early reply.

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