Dynamic Filtering with Large Sampling Field for ConvNets

Understand

We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.

• During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples.
• Such multiple samples enlarge the kernels' receptive fields significantly without requiring more parameters.
• While LS-DFN inherits the advantages of DFN, namely avoiding feature map blurring by position-wise kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs.