  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. Our model is efficient and can be trained end-to-end via
standard back-propagation. We demonstrate the merits of our LS-DFN on both
sparse and dense prediction tasks involving object detection, semantic
segmentation, and flow estimation. Our results show LS-DFN enjoys stronger
recognition abilities in object detection and semantic segmentation tasks on
VOC benchmark and sharper responses in flow estimation on FlyingChairs dataset
compared to strong baselines.
