The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635

Jun 26, 2023
52:31

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Quick takeaways

  • Data augmentation using distractions improves the accuracy of optical flow models for motion estimation.
  • The X3KD model combines data from multiple sensors to achieve more accurate and efficient object detection, setting new state-of-the-art performance in autonomous driving.

Deep dives

Improving Optical Flow with Distract Flow

The Distract Flow paper presents a novel approach to augmenting optical flow motion estimation models without requiring labeled training data. By introducing distractions into the training data, such as blending frames of related content, the model improves its ability to estimate motion in a semantically meaningful manner. The paper demonstrates that this approach improves the accuracy of optical flow models on established benchmarks, making them more robust and efficient.

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