Hello, I have followed the deployment in the readme file, but the results from the run are train_loss = nan, best_eval_score = 0.0114, eval_score = 0.0114. What could be the reason for this?

大佬您好！ 我在运行您的代码时audio_pre.py中的这行audio_feats = pickle.load(f)代码报错，提示ValueError: could not convert string to float，您知道什么原因吗？

I am running the code, but there are no predictions saved in the output folder; all the subfolders inside the output folder are empty.

I've been clone-coded to learn how MIntRec system goes.

While working on Methds >> MAG_BERT >> manager.py, in line 58, I think it should be "for epoch in range" rather than "for epoch in trange".

Though it's trivial, I just wanted to let you guys know. Thank you.

您好！

在论文的表4中，您单独列出了文本模态的识别结果，请问有对其它两个模态做实验嘛？

我对声音和视觉两种模态分别做实验，二分类acc不到60%，所以想问下您这边得到的结果，看哪里出了问题。

谢谢！

您好，我最近在尝试使用您的video_preprocess对我的视频进行处理，但是遇到了几个问题，首先是TalkNet，请问这是要自行寻找的吗

第二个问题是我想知道video_preprocess的输入内容是什么，是视频吗？我已经将视频放在下述的文件夹了，但是依旧没有效果

Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

Attention: please create a new virtual environment for OpenMMLab 2.0.

I followed the Quick start.
But when I do pip install -r requirements.txt, always get error:
ERROR: Could not build wheels for tokenizers, which is required to install pyproject.toml-based projects

Is args.TalkNet_speaker_path an empty folder? args.TalkNet_speaker_path="MIA/datasets/speaker_annotation/Talknet"

您好！MISA.py中这行代码 self._extract_features(video_feats, lengths, self.vrnn1, self.vrnn2, self.vlayer_norm)中的lengths不应该传入的是文本的长度吧？应该是viedo没有填充前的长度吧

I recently read your amazing paper and am interested in exploring improved methods for intent recognition based on its content. To do this, I believe it's crucial to first experience the end-to-end pipeline of this paper's experiment. I appreciate that the code for the experiment has been generously made available, but I also want to experience the process of feature extraction for Raw Audio, Raw Video, and Raw Text.

Would it be possible for you to share the pre-trained model checkpoint that was used in writing this paper and for the related experiment? I noticed in the paper that Bert-base-uncased was used for the Text modality's Feature Extractor, but it seems the details for other modalities have not been disclosed, hence I am leaving this issue.

Thank you once again for writing and sharing such a paper & implementation code

Hi, when will the tutorial for using this code be released. For example, the processing details of the data, and the display of the results, etc. Looking forward to your update.