Video-LLaVA: Better understanding and processing of images and videos
Video-LLaVA: Large-scale visual language models for better understanding and processing of images and videos.
It uses a special technology to convert information in images and videos into a format similar to text, allowing computers to process visual information in the same way as language.
This allows the model to better understand video content as if it were a piece of text.
I tested it and it feels pretty good! But use English 😂
Moreover, the model can process text and visual signals simultaneously, providing a multi-modal understanding capability, which is very useful for automatic question answering systems and other applications that require understanding of complex visual information.
Video-LLaVA performs well on multiple image and video benchmarks, demonstrating its strong adaptability to different types of visual data.
Main working principle features:
- Unified visual representation: Video-LLaVA solves the misalignment problem in previous models when processing visual information by pre-aligning the features of images and videos into a unified visual feature space. This approach enables large language models (LLMs) to learn and understand multimodal (image and video) information more efficiently.
2. Joint training: The model not only pre-aligns image and video features, but also performs joint training on images and videos. This training method helps improve the model’s ability to understand multi-modal information.
3. Efficient model structure: Video-LLaVA includes language binding encoders (LanguageBind encoders) for extracting features of original visual signals (such as images or videos), large language models (such as Vicuna), visual projection layers, and word embeddings layer. These components work together to provide an efficient and powerful visual language processing framework.
4. Superior performance: Video-LLaVA demonstrates superior performance in multiple image and video benchmark tests. It not only surpasses advanced visual language models such as mPLUG-owl-7B and InstructBLIP-7B in image understanding, but also surpasses models specifically designed for videos such as Video-ChatGPT in video understanding.
GitHub
Paper
HuggingFace demo
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