hi when I run code as below:

video_folder=visualization/videos
output_folder=visualization/output
pdvc_model_path=save/anet_tsp_pdvc/model-best.pth
output_language=en
bash test_and_visualize.sh $video_folder $output_folder $pdvc_model_path $output_language

and the error is generated:

**from densevid_eval3.SODA.soda import SODA

ModuleNotFoundError: No module named 'densevid_eval3.SODA.soda' START VISUALIZATION Traceback (most recent call last): File "visualization/visualization.py", line 154, in d = json.load(open(opt.dvc_file))['results'] FileNotFoundError: [Errno 2] No such file or directory: 'visualization/output/generated_captions/dvc_results.json'**

so where is dvc_results.json? and how can i got it?

thanks

Thanks for the great work! I notice that in the Table 4 of your paper, PDVC can achieve "B@4 11.80| M 15.93 | C 27.27" in ActivityNet Captions ae-val set, but it is "B@4 10.18 | M 15.96 | C 20.66" for PDVC with TSN features shown in the Readme. I wonder if the two datasets (ActivityNet Captions v.s. ActivityNet Entity) are different that leads to such different results? Looking forward to your reply.

Hello @ttengwang , I am trying to train your model from scratch (just for learning purpose). However I am facing few issues:

1. the train_caption_file or val_caption_file does not have labels, which is being used in video_dataset.py (also in class loss). Am I using some wrong file?
2. I tried with labels from action_proposal dataset (with captioning related part removed), but the loss_ce doesn't decrease at all, both in train and val (did you face any issues like this?). Also the loss_ce is coming in ranges of 300-400.
3. How many epochs you trained before getting decent captions?

Hi

Thank you for your great work

I loaded your pretrained model and ran your code using my video dataset (SumMe: video summarization benchmark),

but the results are really weird.

most captions doesn't represent visual features

https://user-images.githubusercontent.com/56618962/167997537-5b21d8bc-a9b2-4e97-b735-93dfe36189e3.mp4

I just loaded your models and ran on the video datasets

most video captions are very weird

Did i miss something???

thank you

您好，我想问一下当我用PDVC with learnt proposals 训练出来的结果是与readme中Dense video caption（with learnt proposals）中的结果对比还是跟论文中Predicted proposals对比呢 Predicted proposals与learnt proposals又有什么不同呢？ 麻烦您能回答我这个困惑么

I would like to ask if the evaluation index of the reproduction model of the paper is only described by video paragraphs. If I want to get the evaluation index of dense video caption

Hello professor,

When I reproduced your ‘i3d+vggish’ model, I found that i can not achieve the same results compared with the original paper. I don't know if there is something wrong with my settings.

Thinks

I'm sorry to bother you again, professor. As for the two indicators BLEU4 and CIDEr of Dense Video Caption task in the paper, how can I get these two results？

作者您好~我使用的是TSP的特征以及预训练好的模型，测试predicted proposal时得到与readme中相近结果（bleu4:10.46, METEOR: 16.43, CIDEr: 20.92），这个结果比论文Table 4中的结果要好，可能是因为用了更好的特征。

但当我使用同样的特征和模型测试GT proposal时，得到（bleu4:11.17, METEOR15.58, CIDEr: 22.70），这个结果又明显不如论文Table 4中的结果，这是为什么呢？是测试GT proposal用的模型和测试predicted proposal的模型不一样吗？

如果方便的话，能不能给我发一份模型在predicted proposal和GT proposal两种条件下的预测结果呀，我们打算搜集一些模型的结果进行一个人工评测，我的邮箱是[email protected]，感谢！

Hi @ttengwang ~ Thanks for the sharing of your wonderful work! I want to caption my custom video, but unfortunately I find that most codes for captioning are starting from extracted features, and little instructions are provided for the extraction process. It's very inconvenient for me, cause I'm not so familiar with the captioning task, and I just want to utilize the tool for some applications. So could you please kindly give me some detailed instructions on how I can get the captions from a raw video? I will appreciate it a lot!

Thanks, Zhihong

Thank you so much for this wonderful project. When I tried to run your code on my validation set, I ran into some problems. For example, in a video, a cat runs out of a Christmas gift box, but the prediction is: a woman runs out of the Christmas gift box. Another video of mine shows some sheep walking and the prediction is that some horses are walking. From this, it can be seen that the model can recognize the action, but not the type of the object. I think it may be the problem of ActivityNet, because the animal category in the dataset only contains dogs and horses. Could you please provide a pre training weight obtained after pre-training on ImageNet-22K. I think this may be really effective for the model when it comes to object detection. Finally thank you for your contribution.