SpineNet - Learning Scale-Permuted Backbone for Recognition and Localization

Explore a comprehensive video explanation of the SpineNet paper, which challenges traditional CNN architectures for object detection tasks. Learn about scale-permuted networks, neural architecture search, and how SpineNet improves upon ResNet-FPN models. Discover the innovative approach of using multiple rounds of re-scaling and long-range skip connections to enhance recognition and localization performance. Gain insights into up- and downsampling techniques, ablation studies, and potential future developments like attention routing for CNNs. Understand the significant improvements SpineNet achieves in object detection tasks and its transferability to classification tasks.

- Intro & Overview
- Problem Statement
- The Problem with Current Architectures
- Scale-Permuted Networks
- Neural Architecture Search
- Up- and Downsampling
- From ResNet to SpineNet
- Ablations
- My Idea: Attention Routing for CNNs
- More Experiments
- Conclusion & Comments