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arXiv Computer Vision research summaries for April 01, 2024.
Today's Research Themes (AI-Generated):
• New open-vocabulary SGG framework leverages pre-trained vision-language models for enhanced scene graph generation
• Image-Conditioned Caption Correction task improves zero-shot performance of generative vision-language models without labeled data
• LLaMA-Excitor introduces a lightweight module to stimulate LLM potential while preserving pre-trained knowledge
• TEAR method significantly enhances the scalability and efficiency of outlier-robust 3D registration
• GyroDeblurNet leverages gyro sensor data for state-of-the-art single image deblurring performance
arXiv Computer Vision research summaries for April 01, 2024.
Today's Research Themes (AI-Generated):
• New open-vocabulary SGG framework leverages pre-trained vision-language models for enhanced scene graph generation
• Image-Conditioned Caption Correction task improves zero-shot performance of generative vision-language models without labeled data
• LLaMA-Excitor introduces a lightweight module to stimulate LLM potential while preserving pre-trained knowledge
• TEAR method significantly enhances the scalability and efficiency of outlier-robust 3D registration
• GyroDeblurNet leverages gyro sensor data for state-of-the-art single image deblurring performance