MiniGPT-V2: Solution to multi-tasking problems
MiniGPT-V2: One-stop solution to visual and language multi-tasking problems
MiniGPT-4, which implemented visual capabilities when GPT 4 first came out, has been silent and has recently been updated to the V2 version.
The model is designed to solve a variety of visual-linguistic tasks, including but not limited to image annotation, object parsing and localization, and answering questions in images.
Compared with GPT-4, MiniGPT-4 focuses more on visual-linguistic tasks. It can handle not only plain text data, but also image data, which makes it more capable in multi-modal learning.
MiniGPT-v2 is developed based on Llama2 Chat 7B. Vicuna V0 and Llama 2 versions are provided.
The performance of MiniGPT-v2 on multiple visual question answering (VQA) data sets exceeds that of BLIP-2, LlaVA, Shikra and other models.
Projects and demos: https://t.co/rW5UMdqe0X
Paper: https://t.co/CqOJDf6HHZ
GitHub: https://t.co/E2hiHOVuUP
Online experience: https://t.co/Rz0DTzBwy4
working principle:
iniGPT-4 is a model that combines a visual encoder and a large language model (Vicuna). Its workflow is roughly as follows:
1. Visual encoder:
Image input: When you give the model an image as input, there is first a visual encoder to process the image.
Feature extraction: The visual encoder extracts various useful information or features from the image. These features may include the shape, color, location, etc. of the object.
2. Large language model (Vicuna):
Feature integration: The extracted visual features are sent to a large language model called Vicuna.
Text generation: Vicuna will generate relevant text based on these visual features. These texts may be descriptions of images, stories, poems, or other forms of natural language.
3. Task execution:
Versatility: MiniGPT-4 can perform multiple types of tasks depending on different mission requirements. For example, it can generate cooking instructions based on pictures of food, or write a short story based on pictures of a scene.
Interactivity: In addition to generating text, the model can also solve some problems or challenges in images, increasing interactivity with users.
MiniGPT-v2 is able to perform well on multiple visual-linguistic tasks while also adapting to new, unseen tasks and data.
However, it is worth noting that the model still has certain limitations. For example, the model may produce “hallucination” in some cases, that is, it may incorrectly calibrate or identify non-existent objects in the image.
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