Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
This work addresses the need for reducing annotation costs in semantic segmentation, though it is incremental by building on existing unsupervised methods.
The paper tackled the problem of unsupervised semantic segmentation by incorporating depth information to improve feature correlation and sampling, resulting in significant performance improvements across multiple benchmark datasets.
Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work, we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve this by (1) learning depth-feature correlation by spatially correlate the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) implementing farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene. Finally, we demonstrate the effectiveness of our technical contributions through extensive experimentation and present significant improvements in performance across multiple benchmark datasets.