Abstract
Modern semantic segmentation methods devote much attention to adjusting feature representations to improve the segmentation performance in various ways, such as metric learning, architecture design, etc. However, almost all those methods neglect the particularity of boundary pixels. These pixels are prone to obtain confusing features from both sides due to the continuous expansion of receptive fields in CNN networks. In this way, they will mislead the model optimization direction and make the class weights of such categories that tend to share many adjacent pixels lack discrimination, which will damage the overall performance. In this work, we dive deep into this problem and propose a novel method named Embedded Superpixel CRF (ES-CRF) to address it. ES-CRF involves two main aspects. On the one hand, ES-CRF innovatively fuses the CRF mechanism into the CNN network as an organic whole for more effective end-to-end optimization. It utilizes CRF to guide the message passing between pixels in high-level features to purify the feature representation of boundary pixels, with the help of inner pixels belong to the same object. On the other hand, superpixel is integrated into ES-CRF to exploit the local object prior for more reliable message passing. Finally, our proposed method yields new records on two challenging benchmarks, i.e., Cityscapes and ADE20K. Moreover, we make detailed theoretical analysis to verify the superiority of ES-CRF.
Abstract (translated)
URL
https://arxiv.org/abs/2112.07106
