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Code for ACM MM2020 paper: Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization

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could you please release the features ?thanks

Hi, I am confused about implemention of the loss function used in the paper, this is the function: ![image](https://user-images.githubusercontent.com/19609285/144852487-f61c0e68-09b4-40e4-8541-315c7af76967.png) and I only find the following code: ![image](https://user-images.githubusercontent.com/19609285/144852802-8d8ee246-3199-4218-8c3e-6dad27f315ca.png) Could you share...

Hello, after reading your paper, I have a question I would like to consult. I want to know how formula (6) in your paper is realized in the code,

你好,我使用的是TACOS,有tall_c3d_features.hdf5,但是没有以.npy结尾的特征文件

hello,i noticed that your pretrained model with a subsript like model_4274 on tacos and model_6852 on activity,does it means you finished training the model on 4274 and 6852 epoch respectively?...

"we adopt convolution kernel size of [16, 32, 64, 96, 128, 160, 192] for Activity Caption, and [8, 16, 32, 64] for TACoS." actually, the kernel size is 1 in...

The [model](https://github.com/liudaizong/CSMGAN/blob/master/code/models_/gcn_final.py) implemented by the code is **quite different** (e.g., only one layer Graph) from the model described in the paper. However, the results of the code are consistent with...