Unsupervised Multi-class Joint Image Segmentation

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SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation (2016, methodology): “Inspired by the recent work on synchronization [23, 25, 26, 9], we propose to align these different spectral domains using functional map [21].”
Unsupervised Object Discovery and Tracking in Video Collections (2015, background): “Related approaches have been proposed for salient region detection [18], image cosegmentation [36, 37], and image colo-calization [4].”
Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints (2016, methodology): “Cycle consistency in a graph has been applied to various computer vision and graphics problems, including co-segmentation [28,29], structure from motion [30,31] and image matching [32,33].”
FlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences (2015, background): “Graph consistency: The idea of utilizing consistency constraints within a global graph structure has been applied to various vision and graphics problems, including cosegmentation [40, 41], structure from motion [47, 42], and shape matching [17].”
Temporal Cycle-Consistency Learning (2019, methodology): “It has been applied successfully for tasks like co-segmentation [43, 44], structure from motion [49, 51], and image matching [54, 55, 56].”