Abstract. Superpixels provide an efficient low/mid-level representation
of image data, which greatly reduces the number of image primitives for
subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end
trainable deep neural networks. We develop a new differentiable model
for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting Superpixel Sampling Network (SSN)
is end-to-end trainable, which allows learning task-specific superpixels
with flexible loss functions and has fast runtime. Extensive experimental analysis indicates that SSNs not only outperform existing superpixel
algorithms on traditional segmentation benchmarks, but can also learn
superpixels for other tasks. In addition, SSNs can be easily integrated
into downstream deep networks resulting in performance improvements