18146581. FINE-TUNING JOINT TEXT-IMAGE ENCODERS USING REPROGRAMMING simplified abstract (International Business Machines Corporation)

FINE-TUNING JOINT TEXT-IMAGE ENCODERS USING REPROGRAMMING

Organization Name

International Business Machines Corporation

Inventor(s)

Andrew Geng of Madison WI (US)

Pin-Yu Chen of White Plains NY (US)

FINE-TUNING JOINT TEXT-IMAGE ENCODERS USING REPROGRAMMING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18146581 titled 'FINE-TUNING JOINT TEXT-IMAGE ENCODERS USING REPROGRAMMING

Simplified Explanation

This patent application discusses techniques to fine-tune a joint text-image encoder through model reprogramming. The encoder includes both an image encoder and a text encoder, which are trained using received images and captions. Reprogrammed images and captions are generated based on the received data, and the encoders are further trained using this reprogrammed data. Parameters for the reprogramming functions are backpropagated to enhance the encoder's performance through transfer learning.

• The patent application focuses on fine-tuning a joint text-image encoder through model reprogramming.
• It involves training an image encoder and a text encoder using received images and captions.
• Reprogrammed images and captions are generated based on the received data to further train the encoders.
• Parameters for the reprogramming functions are backpropagated to improve the encoder's performance via transfer learning.

Potential Applications

This technology could be applied in various fields such as image recognition, natural language processing, and multimedia content analysis. It could enhance the performance of systems that require both text and image processing capabilities.

Problems Solved

This technology addresses the need for more accurate and efficient joint text-image encoding. By fine-tuning the encoder through model reprogramming, it aims to improve the overall performance of systems that rely on both text and image data.

Benefits

The benefits of this technology include improved accuracy in text-image processing tasks, enhanced performance of multimedia content analysis systems, and increased efficiency in image recognition and natural language processing applications.

Commercial Applications

This technology could be valuable in industries such as e-commerce, social media, healthcare, and security. It could be used to develop advanced image search engines, content recommendation systems, and automated image captioning tools.

Questions about the Technology

How does model reprogramming enhance the performance of the joint text-image encoder?

Model reprogramming involves generating reprogrammed images and captions based on received data, which are used to further train the encoder. This process helps fine-tune the encoder's parameters and improve its overall performance through transfer learning.

What are the potential applications of this technology beyond text-image encoding?

This technology could have applications in various fields such as image recognition, natural language processing, and multimedia content analysis. It could be used to enhance the performance of systems that require both text and image processing capabilities.

Original Abstract Submitted

Techniques to fine-tune a joint text-image encoder via model reprogramming. The joint text-image encoder includes an image encoder and a text encoder, which are trained. An image and a caption describing the image are received. A reprogrammed image is generated based on the received image and using a first function. A reprogrammed caption is generated based on the received caption and using a second function. The image encoder and the text encoder are further trained using the reprogrammed image and the reprogrammed caption. One or more parameters for each of the first and second functions are backpropagated to produce, via transfer learning, the fine-tuned joint text-image encoder.