Researchers have analyzed CLIP (Contrastive Language-Image Pretraining), a neural network that uses language supervision to acquire visual concepts. They found biases in CLIP models regarding visual text and color. The team studied the LAION-2B dataset and discovered bias in text spotting. They emphasized the impact of parrot captions on CLIP model learning.
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CLIP Models and Visual Text Bias
Research Overview
In a recent study, researchers examined CLIP (Contrastive Language-Image Pretraining), a neural network that uses natural language supervision to acquire visual concepts effectively. While CLIP has advanced vision-language modeling tasks, it has displayed biases related to visual text, color, and gender.
Practical Solutions
The research team utilized picture clustering to address the bias issue, ranking image-text pairs based on CLIP scores. They identified “Parrot Captions” that teach CLIP to recognize text without fully understanding visual concepts. The study also highlighted biases in released CLIP models, particularly in favor of text spotting in web photographs.
Key Findings
The study revealed that CLIP models trained with parrot caption data gain text detection abilities but lose zero-shot generalization ability on image-text tasks. The research emphasized the impact of parrot captions on CLIP model learning and the biases associated with visual text in image captions.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Are CLIP Models ‘Parroting’ Text in Images? This Paper Explores the Text Spotting Bias in Vision-Language Systems
- MarkTechPost
- Twitter – @itinaicom
