Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling

• URL: http://arxiv.org/abs/2309.12378v2
• Date: Tue, 26 Mar 2024 09:31:28 GMT
• Title: Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
• Authors: Leon Sick, Dominik Engel, Pedro Hermosilla, Timo Ropinski,
• Abstract summary: In this work, we build upon advances in unsupervised learning by incorporating information about the structure of a scene into the training process. 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. We then implement farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene.
• Score: 14.88236554564287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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