README.md

metadata

title: LanguageBind
emoji: ⚡
colorFrom: pink
colorTo: blue
sdk: gradio
pinned: false

LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

If you like our project, please give us a star ⭐ on GitHub for latest update.

📰 News

• [2023.10.14] 😱 LanguageBind-Video achieves state-of-the-art (SOTA) performance on 4 datasets, checking our ✨results! The checkpoint have updated on Huggingface Model Hub!
• [2023.10.12] 👀 We are training a stronger LanguageBind-Video model. The checkpoint will also be updated on Huggingface Model Hub soon.
• [2023.10.10] 🎉 We updated the LanguageBind-Audio to exceed ImageBind by 16.2% on the ESC-50 dataset. Sample data can be found in assets, and emergency zero-shot usage is described.
• [2023.10.07] The checkpoints are available on 🤗 Huggingface Model.
• [2023.10.04] Code and demo are available now! Welcome to watch 👀 this repository for the latest updates.

😮 Highlights

💡 High performance, but NO intermediate modality required

LanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics.

• The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.

⚡️ A multimodal, fully aligned and voluminous dataset

We propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities.

• The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.

🔥 Multi-view enhanced description for training

We make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language.

🤗 Demo

• Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.

python gradio_app.py

• Online demo. We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.

  

🚀 Main Results

Video-Language

LanguageBind achieves state-of-the-art (SOTA) performance on four datasets, surpassing InterVideo by 1.9% on MSR-VTT, 8.8% on MSVD, 6.3% on DiDeMo, and 4.4% on ActivityNet. It is worth noting that InterVideo employs more extensive training data, signifying that LanguageBind represents an efficient pretraining method.

Multiple Modalities

Video-Language, Infrared-Language, Depth-Language, and Audio-Language zero-shot classification. We report text-to-audio R@1 for the Clotho dataset and top-1 accuracy for the rest of the datasets.

🛠️ Requirements and Installation

• Python >= 3.8
• Pytorch >= 1.13.0
• CUDA Version >= 10.2 (recommend 11.6)
• Install required packages:

git clone https://github.com/PKU-YuanGroup/LanguageBind
cd LanguageBind
pip install -r requirements.txt

🤖 API

We open source all modalities preprocessing code. If you want to load the model (e.g. LanguageBind/LanguageBind_Thermal) from the model hub on Huggingface or on local, you can use the following code snippets.

Inference for Multi-modal Binding

We have provided some sample datasets in assets to quickly see how languagebind works.

import torch
from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer

if __name__ == '__main__':
    device = 'cuda:0'
    device = torch.device(device)
    clip_type = ('thermal', 'image', 'video', 'depth', 'audio')
    model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
    model = model.to(device)
    model.eval()
    pretrained_ckpt = f'LanguageBind/LanguageBind_Image'
    tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
    modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type}

    image = ['assets/image/0.jpg', 'assets/image/1.jpg']
    audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
    video = ['assets/video/0.mp4', 'assets/video/1.mp4']
    depth = ['assets/depth/0.png', 'assets/depth/1.png']
    thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
    language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']

    inputs = {
        'image': to_device(modality_transform['image'](image), device),
        'video': to_device(modality_transform['video'](video), device),
        'audio': to_device(modality_transform['audio'](audio), device),
        'depth': to_device(modality_transform['depth'](depth), device),
        'thermal': to_device(modality_transform['thermal'](thermal), device),
    }
    inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
                                             truncation=True, return_tensors='pt'), device)
    with torch.no_grad():
        embeddings = model(inputs)
    print("Video x Text: \n",
          torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Image x Text: \n",
          torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Depth x Text: \n",
          torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Audio x Text: \n",
          torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Thermal x Text: \n",
          torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())

Then returns the following result.

Video x Text: 
 [[9.9999988e-01 1.5560659e-07]
 [6.2432008e-08 9.9999988e-01]]
Image x Text: 
 [[1.0000000e+00 4.0599781e-09]
 [1.2165208e-08 1.0000000e+00]]
Depth x Text: 
 [[9.9952829e-01 4.7178473e-04]
 [1.6411507e-01 8.3588487e-01]]
Audio x Text: 
 [[0.9977429  0.00225709]
 [0.00996918 0.99003077]]
Thermal x Text: 
 [[0.9744922  0.02550781]
 [0.3656127  0.6343873 ]]

Emergency zero-shot

Since languagebind binds each modality together, we also found the emergency zero-shot. It's very simple to use.

print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy())
print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy())

Then, you will get:

Video x Audio: 
 [[1. 0.]
 [0. 1.]]
Image x Depth: 
 [[1. 0.]
 [0. 1.]]
Image x Thermal: 
 [[1. 0.]
 [0. 1.]]

Different branches for X-Language task

Additionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.

Thermal

import torch
from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal'
model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
thermal_process = LanguageBindThermalProcessor(model.config, tokenizer)

model.eval()
data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

Depth

import torch
from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Depth'
model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
depth_process = LanguageBindDepthProcessor(model.config, tokenizer)

model.eval()
data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

Video

import torch
from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Video'
model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
video_process = LanguageBindVideoProcessor(model.config, tokenizer)

model.eval()
data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

Audio

import torch
from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Audio'
model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
audio_process = LanguageBindAudioProcessor(model.config, tokenizer)

model.eval()
data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

Image

import torch
from languagebind import LanguageBindImage,  LanguageBindImageTokenizer,  LanguageBindImageProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Image'
model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
image_process = LanguageBindImageProcessor(model.config, tokenizer)

model.eval()
data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

💥 VIDAL-10M

The datasets is in DATASETS.md.

🗝️ Training & Validating

The training & validating instruction is in TRAIN_AND_VALIDATE.md.

👍 Acknowledgement

• OpenCLIP An open source pretraining framework.
• CLIP4Clip An open source Video-Text retrieval framework.
• sRGB-TIR An open source framework to generate infrared (thermal) images.
• GLPN An open source framework to generate depth images.

🔒 License

• The majority of this project is released under the MIT license as found in the LICENSE file.
• The dataset of this project is released under the CC-BY-NC 4.0 license as found in the DATASET_LICENSE file.

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.

@misc{zhu2023languagebind,
      title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, 
      author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan},
      year={2023},
      eprint={2310.01852},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}