Image Captioning with MAGIC

author: David Wang

Description

This operator generates the caption with MAGIC which describes the content of the given image. MAGIC is a simple yet efficient plug-and-play framework, which directly combines an off-the-shelf LM (i.e., GPT-2) and an image-text matching model (i.e., CLIP) for image-grounded text generation. During decoding, MAGIC influences the generation of the LM by introducing a CLIP-induced score, called magic score, which regularizes the generated result to be semantically related to a given image while being coherent to the previously generated context. This is an adaptation from yxuansu / MAGIC.

Code Example

Load an image from path './image.jpg' to generate the caption.

Write the pipeline in simplified style:

import towhee

towhee.glob('./image.jpg') \
      .image_decode() \
      .image_captioning.magic(model_name='expansionnet_rf') \
      .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('./image.jpg') \
      .image_decode['path', 'img']() \
      .image_captioning.magic['img', 'text'](model_name='expansionnet_rf') \
      .select['img', 'text']() \
      .show()

Factory Constructor

Create the operator via the following factory method

expansionnet_v2(model_name)

Parameters:

​ model_name: str

​ The model name of MAGIC. Supported model names:

• magic_mscoco

Interface

An image-text embedding operator takes a towhee image as input and generate the correspoing caption.

Parameters:

​ data: towhee.types.Image (a sub-class of numpy.ndarray)

​ The image to generate embedding.

Returns: str

​ The caption generated by model.