

Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635
Jun 26, 2023
Fatih Porikli, Senior Director of Technology at Qualcomm AI Research, shares insights from over 30 years in computer vision. He explores cutting-edge topics such as data augmentation techniques, optimized architectures, and advances in optical flow for video analysis. The conversation delves into the use of language models for fine-grained labeling, enhancing 3D object detection, and the role of generative AI in model efficiency. Fatih also discusses training neural networks and innovative approaches to integrating various data sources for improved accuracy.
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Improved Model Robustness
- DistractFlow improves optical flow models by making them more robust to distractions.
- This is demonstrated by a reduction in endpoint error on benchmarks like Sintel and KITTI.
X3KD for 3D Object Detection
- X3KD is a knowledge distillation technique for 3D object detection used in autonomous driving.
- It uses data from multiple cameras and LiDAR during training but only images during runtime.
Knowledge Distillation Explained
- Knowledge distillation involves training a smaller "student" network to mimic a larger, more capable "teacher" network.
- The goal is to achieve comparable accuracy with a more efficient model.