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Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

License: MIT License

Python 100.00%
deep-learning foreground-action-consistency pytorch temporal-action-localization weakly-supervised-learning

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fac-net's Issues

关于训练

你好,我是一个学生,最近在复现您的论文,但是我使用您的代码,训练了50000轮后,mAP在任何iou下都降为了0,请问您遇到这样的情况了吗

requirements.txt

Hello, Thanks for making your interesting work open to the public.
Could you please provide a requirements.txt?

How to reproduce your results

The first three lines are the result of my own training model:
100 100 34.18 28.70 20.20 13.54 8.17 3.40 1.45
35 70 31.39 26.72 19.16 12.83 7.28 3.66 1.70
35 75 31.38 26.49 19.76 13.25 7.76 3.84 1.81

yours:
1000 1000 68.19 62.43 53.21 44.78 34.10 23.13 13.44

Visualization

Hello, Thanks for making your interesting work open to the public,
I am a student recently studying on WS-TAL tasks.
What I want to ask is about "how to visualize results from network so that it can be the one shown in the paper."
For example, like the ones in the Figure 1 and 5 in your paper.

Thanks, Joseph

ActivityNet V1.3数据集的缺失问题

您好!
我在您的utils/video_dataloader.py文件里面,有看到-I3D-JOINTFeatures.npy文件:

self.path_to_features = args.dataset_root + '%s/%s-%s-JOINTFeatures.npy' % (

我想问一下,您是否可以提供一下ActivityNet1.3-I3D-JOINTFeatures.npy文件和ActivityNet1.3-Annotation文件夹里面的文件呢?

或者我如何制作一个总的ActivityNet1.3-I3D-JOINTFeatures.np文件以及ActivityNet1.3-Annotation文件夹里面的classlist.npy、labels_all.npy等文件呢?

冒昧打扰,十分抱歉!

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