Contrastive language image pretraining (CLIP) is a standard method for training vision-language modelsContrastive language image pretraining (CLIP) is a standard method for training vision-language models.
Apple Machine Learning Research 12:24 am on June 3, 2024
CLIP integrates task-specific models from Model Zoo to enhance its visual capabilities without compromising existing proficiencies, showing substantial improvements in various vision tasks. This research paper presents CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement.
- Integration of Task-specific Models: The study explores the augmentation of CLIP with task-specific models from model zoos to generate pseudo-labels, leading to significant improvements across multiple vision tasks.
- Enhanced Vision Capabilities: By incorporating these models into the training process alongside contrastive learning, CLIP achieves better object localization and understanding without sacrificing its strengths in promptable zero-shot classification.
- Diverse Task Performance Improvement: The pseudo-supervised approach shows up to a 16.3% performance boost across various tasks, including segmentation, detection, depth estimation, and surface normal estimation.
- Research Acceptance: The paper has been accepted for presentation at NeurIPS 2023 Workshop in UniReps and the eLVM Workshop at CVPR 2024. It further contributes to existing literature with similar studies, like SAM-CLIP.
- Contributions to Machine Learning: The findings contribute to ongoing advancements in machine learning by leveraging open model zoos and pseudo-supervision techniques for vision models.
https://machinelearning.apple.com/research/clip-model-zoo-experts
< Previous Story - Next Story >