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As i clearly understand, demo_vid2seq.py is used for main goal: video chapter generation.
How can i change this module for dense video captioning purposes? Or can you add new demo for this inference…
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Great work Antoine! In your last paper Vid2Seq, you also tested the pre-trained model on the ActivityNet captions dataset, but in VidChapters you only show on ViTT and YouCook2. I am wondering if ther…
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Hi @xingyizhou,@a-nagrani and @antoyang,
I'm writing to you because I'm interested in using the Vid2Seq model for dense captioning and video captioning on a few educational videos which are MP4 fil…
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hi!
may i know how to do the inference without speech?
I've set the --no_speech but so that the output is [].
And when i do inference in activitynet and charades dataset, the output looks like it…
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Hi, Dr. Jian:
Thanks for this video repo. I tried to reproduce the report result but still have two problems:
1. In "lavis/projects/blip2/train/caption_vatex_stage1.yaml", I gave the param…
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Hey,
I would like to modify your code for my own dataset with already generated custom captions.
The clips show every time one Person doing one out of nine actions. The captions describe the gender,…
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I can’t find code for any datasets other than synthetic and TEMPO. Additionally, when I download the data from your page, Charades is missing splits, annotations, etc. (everything other than feats) an…
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**System information**
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): "18.04.1 LTS…
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Thanks for the great work! I have a question about the way to evaluate the model on paragraph captioning: do you fine-tune the pre-trained checkpoint on the paragraph captioning task, or just remove t…