Learning More May Not Be Better: Knowledge Transferability in Vision-and-Language Tasks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision
• Natural Language Processing

Background:

• The prevailing trend in machine learning assumes that aggregating diverse datasets enhances overall model performance.
• However, the effectiveness of knowledge transfer across different tasks, even with shared objectives, remains underexplored.

Purpose of the Study:

• To investigate the nuances of knowledge transferability in vision-and-language models.
• To determine if increased knowledge acquisition universally leads to improved performance in multi-modal tasks.

Main Methods:

• Conducted extensive cross-experiments involving hundreds of trials.
• Analyzed twelve distinct vision-and-language tasks, grouped into four categories based on task relatedness.

Main Results:

• Demonstrated that knowledge transfer is not always beneficial; some knowledge negatively impacts related tasks.
• Observed that tasks within the same group do not consistently improve each other through knowledge transfer.
• Identified dataset size and the pre-training stage as significant factors influencing knowledge transfer effectiveness.

Conclusions:

• The assumption that more knowledge is always better for vision-and-language models is challenged.
• Effective knowledge transfer in multi-modal learning is complex and task-dependent, influenced by factors beyond task similarity.