Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks... One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark. read more

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Real-Time Semantic Segmentation Cityscapes test CRF-RNN mIoU 62.5% # 28
Time (ms) 700 # 20
Frame (fps) 1.4 # 24
Semantic Segmentation Cityscapes test CRF-RNN Mean IoU (class) 62.5% # 87
Semantic Segmentation PASCAL VOC 2012 test CRF-RNN Mean IoU 74.7% # 41

Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Semantic Segmentation PASCAL Context CRF-RNN mIoU 39.3 # 41