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Languagebind Video FT

Developed by LanguageBind
LanguageBind is a language-centric multimodal pretraining method that uses language as the bond between different modalities to achieve semantic alignment across video, infrared, depth, audio, and other modalities.
Downloads 22.97k
Release Time : 11/26/2023

Model Overview

LanguageBind is an innovative multimodal pretraining framework that achieves semantic alignment between language and various modalities such as video, infrared, depth, and audio by treating language as the core bond. This method was published at ICLR 2024 and demonstrates outstanding performance on multimodal tasks.

Model Features

Language-Centric Multimodal Alignment
Uses language as the bond between different modalities, leveraging the rich semantic information of language to achieve multimodal alignment.
Large-Scale Multimodal Dataset
Proposes the VIDAL-10M dataset, containing 10 million data points covering video, infrared, depth, audio, and their corresponding language.
Multi-Perspective Enhanced Training
Generates multi-perspective descriptions by combining metadata, spatial, and temporal information, and enhances language semantics using ChatGPT.
Easy Scalability
The architecture supports easy extension to segmentation, detection tasks, and potentially unlimited modalities.

Model Capabilities

Video-Language understanding
Audio-Language understanding
Infrared-Language understanding
Depth-Language understanding
Cross-modal retrieval
Multimodal semantic alignment

Use Cases

Video Understanding
Video Content Retrieval
Retrieve relevant video content based on text descriptions
Achieves SOTA performance on multiple benchmarks
Audio Understanding
Audio Event Recognition
Identify event types based on audio content
Achieves SOTA performance on 5 datasets
Multimodal Interaction
Cross-Modal Retrieval
Enable mutual retrieval between video, audio, depth, infrared, and text
Achieves efficient cross-modal retrieval through language bonding
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